Autonomous electric vehicle charging

ABSTRACT

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of the filing dateof the following applications: (1) provisional U.S. Patent ApplicationNo. 62/286,017 entitled “Autonomous Vehicle Routing, Maintenance, &Fault Determination,” filed on Jan. 22, 2016; (2) provisional U.S.Patent Application No. 62/287,659 entitled “Autonomous VehicleTechnology,” filed on Jan. 27, 2016; (3) provisional U.S. PatentApplication No. 62/302,990 entitled “Autonomous Vehicle Routing,” filedon Mar. 3, 2016; (4) provisional U.S. Patent Application No. 62/303,500entitled “Autonomous Vehicle Routing,” filed on Mar. 4, 2016; (5)provisional U.S. Patent Application No. 62/312,109 entitled “AutonomousVehicle Routing,” filed on Mar. 23, 2016; (6) provisional U.S. PatentApplication No. 62/349,884 entitled “Autonomous Vehicle Component andSystem Assessment,” filed on Jun. 14, 2016; (7) provisional U.S. PatentApplication No. 62/351,559 entitled “Autonomous Vehicle Component andSystem Assessment,” filed on Jun. 17, 2016; (8) provisional U.S. PatentApplication No. 62/373,084 entitled “Autonomous Vehicle Communications,”filed on Aug. 10, 2016; (9) provisional U.S. Patent Application No.62/376,044 entitled “Autonomous Operation Expansion through Caravans,”filed on Aug. 17, 2016; (10) provisional U.S. Patent Application No.62/380,686 entitled “Autonomous Operation Expansion through Caravans,”filed on Aug. 29, 2016; (11) provisional U.S. Patent Application No.62/381,848 entitled “System and Method for Autonomous Vehicle SharingUsing Facial Recognition,” filed on Aug. 31, 2016; (12) provisional U.S.Patent Application No. 62/406,595 entitled “Autonomous Vehicle ActionCommunications,” filed on Oct. 11, 2016; (13) provisional U.S. PatentApplication No. 62/406,600 entitled “Autonomous Vehicle PathCoordination,” filed on Oct. 11, 2016; (14) provisional U.S. PatentApplication No. 62/406,605 entitled “Autonomous Vehicle Signal Control,”filed on Oct. 11, 2016; (15) provisional U.S. Patent Application No.62/406,611 entitled “Autonomous Vehicle Application,” filed on Oct. 11,2016; (16) provisional U.S. Patent Application No. 62/415,668 entitled“Method and System for Enhancing the Functionality of a Vehicle,” filedon Nov. 1, 2016; (17) provisional U.S. Patent Application No. 62/415,672entitled “Method and System for Repairing a Malfunctioning AutonomousVehicle,” filed on Nov. 1, 2016; (18) provisional U.S. PatentApplication No. 62/415,673 entitled “System and Method for AutonomousVehicle Sharing Using Facial Recognition,” filed on Nov. 1, 2016; (19)provisional U.S. Patent Application No. 62/415,678 entitled “System andMethod for Autonomous Vehicle Ride Sharing Using Facial Recognition,”filed on Nov. 1, 2016; (20) provisional U.S. Patent Application No.62/418,988 entitled “Virtual Testing of Autonomous Vehicle ControlSystem,” filed on Nov. 8, 2016; (21) provisional U.S. Patent ApplicationNo. 62/418,999 entitled “Detecting and Responding to Autonomous VehicleCollisions,” filed on Nov. 8, 2016; (22) provisional U.S. PatentApplication No. 62/419,002 entitled “Automatic Repair on AutonomousVehicles,” filed on Nov. 8, 2016; (23) provisional U.S. PatentApplication No. 62/419,009 entitled “Autonomous Vehicle ComponentMalfunction Impact Assessment,” filed on Nov. 8, 2016; (24) provisionalU.S. Patent Application No. 62/419,017 entitled “Autonomous VehicleSensor Malfunction Detection,” filed on Nov. 8, 2016; (25) provisionalU.S. Patent Application No. 62/419,023 entitled “Autonomous VehicleDamage and Salvage Assessment,” filed on Nov. 8, 2016; (26) provisionalU.S. Patent Application No. 62/424,078 entitled “Systems and Methods forSensor Monitoring,” filed Nov. 18, 2016; (27) provisional U.S. PatentApplication No. 62/424,093 entitled “Autonomous Vehicle SensorMalfunction Detection,” filed on Nov. 18, 2016; (28) provisional U.S.Patent Application No. 62/428,843 entitled “Autonomous Vehicle Control,”filed on Dec. 1, 2016; (29) provisional U.S. Patent Application No.62/430,215 entitled Autonomous Vehicle Environment and ComponentMonitoring,” filed on Dec. 5, 2016; (30) provisional U.S. PatentApplication No. 62/434,355 entitled “Virtual Testing of AutonomousEnvironment Control System,” filed Dec. 14, 2016; (31) provisional U.S.Patent Application No. 62/434,359 entitled “Detecting and Responding toAutonomous Environment Incidents,” filed Dec. 14, 2016; (32) provisionalU.S. Patent Application No. 62/434,361 entitled “Component Damage andSalvage Assessment,” filed Dec. 14, 2016; (33) provisional U.S. PatentApplication No. 62/434,365 entitled “Sensor Malfunction Detection,”filed Dec. 14, 2016; (34) provisional U.S. Patent Application No.62/434,368 entitled “Component Malfunction Impact Assessment,” filedDec. 14, 2016; and (35) provisional U.S. Patent Application No.62/434,370 entitled “Automatic Repair of Autonomous Components,” filedDec. 14, 2016. The entire contents of each of the preceding applicationsare hereby expressly incorporated herein by reference.

Additionally, the present application is related to the followingco-pending U.S. patent applications: (1) U.S. patent application Ser.No. 15/409,143 entitled “Autonomous Operation Suitability Assessment andMapping,” filed Jan. 18, 2017; (2) U.S. patent application Ser. No.15/409,146 entitled “Autonomous Vehicle Routing,” filed Jan. 18, 2017;(3) U.S. patent application Ser. No. 15/409,149 entitled “AutonomousVehicle Routing During Emergencies,” filed Jan. 18, 2017; (4) U.S.patent application Ser. No. 15/409,159 entitled “Autonomous Vehicle TripRouting,” filed Jan. 18, 2017; (5) U.S. patent application Ser. No.15/409,163 entitled “Autonomous Vehicle Parking,” filed Jan. 18, 2017;(6) U.S. patent application Ser. No. 15/409,167 entitled “AutonomousVehicle Retrieval,” filed Jan. 18, 2017; (7) U.S. patent applicationSer. No. 15/409,092 entitled “Autonomous Vehicle Action Communications,”filed Jan. 18, 2017; (8) U.S. patent application Ser. No. 15/409,099entitled “Autonomous Vehicle Path Coordination,” filed Jan. 18, 2017;(9) U.S. patent application Ser. No. 15/409,107 entitled “AutonomousVehicle Signal Control,” filed Jan. 18, 2017; (10) U.S. patentapplication Ser. No. 15/409,115 entitled “Autonomous VehicleApplication,” filed Jan. 18, 2017; (11) U.S. patent application Ser. No.15/409,136 entitled “Method and System for Enhancing the Functionalityof a Vehicle,” filed Jan. 18, 2017; (12) U.S. patent application Ser.No. 15/409,180 entitled “Method and System for Repairing aMalfunctioning Autonomous Vehicle,” filed Jan. 18, 2017; (13) U.S.patent application Ser. No. 15/409,148 entitled “System and Method forAutonomous Vehicle Sharing Using Facial Recognition,” filed Jan. 18,2017; (14) U.S. patent application Ser. No. 15/409,198 entitled “Systemand Method for Autonomous Vehicle Ride Sharing Using FacialRecognition,” filed Jan. 18, 2017; (15) U.S. patent application Ser. No.15/409,215 entitled “Autonomous Vehicle Sensor Malfunction Detection,”filed Jan. 18, 2017; (16) U.S. patent application Ser. No. 15/409,248entitled “Sensor Malfunction Detection,” filed Jan. 18, 2017; (17) U.S.patent application Ser. No. 15/409,271 entitled “Autonomous VehicleComponent Malfunction Impact Assessment,” filed Jan. 18, 2017; (18) U.S.patent application Ser. No. 15/409,305 entitled “Component MalfunctionImpact Assessment,” filed Jan. 18, 2017; (19) U.S. patent applicationSer. No. 15/409,318 entitled “Automatic Repair of Autonomous Vehicles,”filed Jan. 18, 2017; (20) U.S. patent application Ser. No. 15/409,336entitled “Automatic Repair of Autonomous Components,” filed Jan. 18,2017; (21) U.S. patent application Ser. No. 15/409,340 entitled“Autonomous Vehicle Damage and Salvage Assessment,” filed Jan. 18, 2017;(22) U.S. patent application Ser. No. 15/409,349 entitled “ComponentDamage and Salvage Assessment,” filed Jan. 18, 2017; (23) U.S. patentapplication Ser. No. 15/409,359 entitled “Detecting and Responding toAutonomous Vehicle Collisions,” filed Jan. 18, 2017; (24) U.S. patentapplication Ser. No. 15/409,371 entitled “Detecting and Responding toAutonomous Environment Incidents,” filed Jan. 18, 2017; (25) U.S. patentapplication Ser. No. 15/409,445 entitled “Virtual Testing of AutonomousVehicle Control System,” filed Jan. 18, 2017; (26) U.S. patentapplication Ser. No. 15/409,473 entitled “Virtual Testing of AutonomousEnvironment Control System,” filed Jan. 18, 2017; (27) U.S. patentapplication Ser. No. 15/409,213 entitled “Coordinated Autonomous VehicleAutomatic Area Scanning,” filed Jan. 18, 2017; (28) U.S. patentapplication Ser. No. 15/409,228 entitled “Operator-SpecificConfiguration of Autonomous Vehicle Operation,” filed Jan. 18, 2017;(29) U.S. patent application Ser. No. 15/409,236 entitled “AutonomousVehicle Operation Adjustment Based Upon Route,” filed Jan. 18, 2017;(30) U.S. patent application Ser. No. 15/409,239 entitled “AutonomousVehicle Component Maintenance and Repair,” filed Jan. 18, 2017; and (31)U.S. patent application Ser. No. 15/409,243 entitled “AnomalousCondition Detection and Response for Autonomous Vehicles,” filed Jan.18, 2017.

FIELD

The present disclosure generally relates to systems and methods forautonomous or semi-autonomous vehicle control, including data analysis,route determination, and automatic adjustment of autonomous operationfeatures.

BACKGROUND

Vehicles are typically operated by a human vehicle operator who controlsboth steering and motive controls. Operator error, inattention,inexperience, misuse, or distraction leads to many vehicle collisionseach year, resulting in injury and damage. Autonomous or semi-autonomousvehicles augment vehicle operators' information or replace vehicleoperators' control commands to operate the vehicle, in whole or part,with computer systems based upon information from sensors within, orattached to, the vehicle. Such vehicles may be operated with or withoutpassengers, thus requiring different means of control than traditionalvehicles. Such vehicles also may include a plurality of advancedsensors, capable of providing significantly more data (both in type andquantity) than is available even from GPS navigation assistance systemsinstalled in traditional vehicles.

Ensuring safe operation of such autonomous or semi-autonomous vehiclesis of the utmost importance because the automated systems of thesevehicles may not function properly in all environments. Althoughautonomous operation may be safer than manual operation under ordinarydriving conditions, unusual or irregular environmental conditions maysignificantly impair the functioning of the autonomous operationfeatures controlling the autonomous vehicle. Under some conditions,autonomous operation may become impractical or excessively dangerous. Asan example, fog or heavy rain may greatly reduce the ability ofautonomous operation features to safely control the vehicle.Additionally, damage or other impairment of sensors or other componentsof autonomous systems may significantly increase the risks associatedwith autonomous operation. Such conditions may change frequently,thereby changing the safety of autonomous vehicle operation.

BRIEF SUMMARY

The present embodiments may be related to autonomous or semi-autonomousvehicle operation, including driverless operation of fully autonomousvehicles. The embodiments described herein relate particularly tovarious aspects of route determination and navigation of autonomousvehicles. This may include determining suitability of roads or roadsegments for varying levels of autonomous operation, which may includegenerating maps indicating roadway suitability for autonomous operation.This may further include route planning, adjustment, or optimization,including risk management by avoidance of road segments associated withhigh risk levels for vehicle accidents involving autonomous vehicles.This may yet further include autonomous route generation and/orimplementation in emergency or non-emergency situations. Yet furtherembodiments may be related to parking autonomous vehicles and retrievingparked autonomous vehicles, which may similarly involve autonomous routedetermination and/or vehicle control.

In one aspect, a computer-implemented method for automaticallyrecharging an autonomous electric vehicle may be provided. The methodmay include (1) detecting charge information associated with a chargelevel of a battery of the autonomous electric vehicle; (2) determiningthe charge level of the battery based upon the charge information; (3)generating a predicted use profile for the autonomous electric vehiclebased upon prior vehicle use data; (4) determining a time and a locationat which to charge the battery based upon the charge level and thepredicted use profile; (5) controlling the autonomous electric vehicleto travel fully autonomously to the determined location at thedetermined time; (6) causing the battery of the autonomous electricvehicle to charge at the location; (7) determining a return location forthe vehicle based upon the predicted use profile; and/or (8) controllingthe autonomous electric vehicle to travel fully autonomously to thereturn location. The return location may be determined based upon thepredicted use profile and is distinct from a prior location from whichthe autonomous electric vehicle travels to the location at which tocharge the battery. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

For instance, the method may determine the charge information when theautonomous electric vehicle is not in use, in which case the determinedtime may be a current time at which the time and location aredetermined. The method may further include determining that the chargelevel is below a maximum recharging threshold, in which case the timeand location may be determined when the charge level is below themaximum recharging threshold. The predicted use profile may indicate anext predicted use of the autonomous electric vehicle, in which case thetime and location may be determined when sufficient time exists torecharge the battery before the next predicted use. The predicted useprofile may indicate a plurality of use periods and use locations overat least one day.

The location at which to charge the battery may be associated with acharging station selected from a plurality of charging stations based atleast in part upon availability of the selected charging station. Themethod may further comprise identifying a current location of theautonomous electric vehicle using a geolocation component within theautonomous electric vehicle and identifying one or more chargingstations in an area surrounding the current location from a databaseincluding location data for a plurality of charging stations. Thelocation at which to charge the battery may be selected from thelocation data associated with the one or more charging stations based atleast in part upon distance from the current location. In furtherembodiments, the method may include accessing map data containing mapinformation regarding a plurality of road segments, which mapinformation may include location data associated with each road segmentand an indication of suitability for autonomous operation feature useassociated with each road segment. A route consisting of one or moreroad segments may be identified from the plurality of road segmentsbetween the current location and the location at which to charge thebattery. The autonomous electric vehicle may then be controlled totravel fully autonomously to the determined location by controlling theautonomous electric vehicle along the identified route.

In some embodiments, the charge information may be determined when theautonomous electric vehicle is in use. In such instances, the predicteduse profile may include one or more predicted breaks in vehicleoperation, each predicted break being associated with a break time and abreak location. The time and location may then be determined based uponthe one or more predicted breaks.

Systems or computer-readable media storing instructions for implementingall or part of the system described above may also be provided in someaspects. Systems for implementing such methods may include one or moreof the following: a mobile computing device, an on-board computer, aremote server, one or more sensors, one or more communication modulesconfigured to communicate wirelessly via radio links, radio frequencylinks, and/or wireless communication channels, and/or one or moreprogram memories coupled to one or more processors of the mobilecomputing device, on-board computer, or remote server. Such programmemories may store instructions to cause the one or more processors toimplement part or all of the method described above. Additional oralternative features described herein below may be included in someaspects.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

The figures described below depict various aspects of the applications,methods, and systems disclosed herein. It should be understood that eachfigure depicts an embodiment of a particular aspect of the disclosedapplications, systems and methods, and that each of the figures isintended to accord with a possible embodiment thereof. Furthermore,wherever possible, the following description refers to the referencenumerals included in the following figures, in which features depictedin multiple figures are designated with consistent reference numerals.

FIG. 1A illustrates a block diagram of an exemplary autonomous vehicledata system for autonomous vehicle operation, monitoring, and relatedfunctions;

FIG. 1B illustrates a block diagram of an exemplary autonomous vehiclemonitoring system, showing a plurality of vehicles and smartinfrastructure components;

FIG. 2 illustrates a block diagram of an exemplary on-board computer ormobile device;

FIG. 3 illustrates a flow diagram of an exemplary autonomous vehicleoperation method;

FIGS. 4A-B illustrate flow diagrams of exemplary autonomous vehicleoperation monitoring methods for obtaining and recording informationduring vehicle operation;

FIG. 5 illustrates a flow diagram of an exemplary autonomous operationsuitability mapping method for determining the suitability of variouslocations for autonomous and/or semi-autonomous operation of vehicles;

FIG. 6 illustrates a flow diagram of an exemplary autonomous vehiclerouting method for determining a route between predetermined locationsto provide autonomous vehicle navigation support;

FIG. 7 illustrates a flow diagram of an exemplary automatic usageoptimization method for monitoring and adjusting autonomous operationfeature usage levels of a vehicle;

FIG. 8 illustrates a flow diagram of an exemplary manual vehicle controlrestriction method for limiting manual control of a vehicle;

FIG. 9 illustrates a flow diagram of an exemplary automatic refueling orrecharging method for fully autonomous vehicles; and

FIG. 10 illustrates a flow diagram of an exemplary passive searchingmethod for automatically searching an area for vehicles, people, orother items using sensor data from a plurality of vehicles.

DETAILED DESCRIPTION

The systems and methods disclosed herein generally relate to collecting,communicating, evaluating, predicting, and/or utilizing data associatedwith autonomous or semi-autonomous operation features for controlling avehicle. The autonomous operation features may take full control of thevehicle under certain conditions, viz. fully autonomous operation, orthe autonomous operation features may assist the vehicle operator inoperating the vehicle, viz. partially autonomous operation. Fullyautonomous operation features may include systems within the vehiclethat pilot the vehicle to a destination with or without a vehicleoperator present (e.g., an operating system for a driverless car).Partially autonomous operation features may assist the vehicle operatorin limited ways (e.g., automatic braking or collision avoidancesystems). Fully or partially autonomous operation features may performspecific functions to control or assist in controlling some aspect ofvehicle operation, or such features may manage or control otherautonomous operation features. For example, a vehicle operating systemmay control numerous subsystems that each fully or partially controlsaspects of vehicle operation.

Optimal route planning for fully or partially autonomous vehicles may beprovided using the systems and methods described herein. A user mayinput an origin and a destination (e.g., A and B locations), whetherthey want to drive fully autonomous or take the fastest route, and/orwhether they will need to park the vehicle nearby or close to thedestination. Routes may be optimized for private passengers based uponroad safety for autonomous vehicles (e.g., predetermined “safe forautonomous vehicle” roads), whether or not the roads allow autonomousvehicles, or other factors (e.g., routes with the least manualintervention required, fastest routes, etc.). Alerts may be provided orgenerated when the autonomous vehicle is approaching an area or roadwhere manual intervention may be needed. Optimal routes may also bedetermined for carpooling or vehicle sharing, delivery or othercommercial use, emergency response (e.g., a “self-driving to hospital”mode), non-driving passenger pick-up and drop-off (e.g., children,elderly, etc.), autonomous parking and retrieval, or other purposes. Insome embodiments, vehicle-infrastructure technology may be used and/orcollect data to develop a most efficient/safest route. The presence ofsmart stoplights, railroad crossings, and other infrastructure may bemapped, and routes may be optimized to include traveling by the mostincidences of smart infrastructure.

In addition to information regarding the position or movement of avehicle, autonomous operation features may collect and utilize otherinformation, such as data about other vehicles or control decisions ofthe vehicle. Such additional information may be used to improve vehicleoperation, route the vehicle to a destination, warn of componentmalfunctions, advise others of potential hazards, or for other purposesdescribed herein. Information may be collected, assessed, and/or sharedvia applications installed and executing on computing devices associatedwith various vehicles or vehicle operators, such as on-board computersof vehicles or smartphones of vehicle operators. By using computerapplications to obtain data, the additional information generated byautonomous vehicles or features may be used to assess the autonomousfeatures themselves while in operation or to provide pertinentinformation to non-autonomous vehicles through an electroniccommunication network. These and other advantages are further describedbelow.

Autonomous operation features utilize data not available to a humanoperator, respond to conditions in the vehicle operating environmentfaster than human operators, and do not suffer fatigue or distraction.Thus, the autonomous operation features may also significantly affectvarious risks associated with operating a vehicle. Moreover,combinations of autonomous operation features may further affectoperating risks due to synergies or conflicts between features. Toaccount for these effects on risk, some embodiments evaluate the qualityof each autonomous operation feature and/or combination of features.This may be accomplished by testing the features and combinations incontrolled environments, as well as analyzing the effectiveness of thefeatures in the ordinary course of vehicle operation. New autonomousoperation features may be evaluated based upon controlled testing and/orestimating ordinary-course performance based upon data regarding othersimilar features for which ordinary-course performance is known.

Some autonomous operation features may be adapted for use underparticular conditions, such as city driving or highway driving.Additionally, the vehicle operator may be able to configure settingsrelating to the features or may enable or disable the features at will.Therefore, some embodiments monitor use of the autonomous operationfeatures, which may include the settings or levels of feature use duringvehicle operation. Information obtained by monitoring feature usage maybe used to determine risk levels associated with vehicle operation,either generally or in relation to a vehicle operator. In suchsituations, total risk may be determined by a weighted combination ofthe risk levels associated with operation while autonomous operationfeatures are enabled (with relevant settings) and the risk levelsassociated with operation while autonomous operation features aredisabled. For fully autonomous vehicles, settings or configurationsrelating to vehicle operation may be monitored and used in determiningvehicle operating risk.

In some embodiments, information regarding the risks associated withvehicle operation with and without the autonomous operation features maybe used to determine risk categories or premiums for a vehicle insurancepolicy covering a vehicle with autonomous operation features, asdescribed elsewhere herein. Risk category or price may be determinedbased upon factors relating to the evaluated effectiveness of theautonomous vehicle features. The risk or price determination may alsoinclude traditional factors, such as location, vehicle type, and levelof vehicle use. For fully autonomous vehicles, factors relating tovehicle operators may be excluded entirely. For partially autonomousvehicles, factors relating to vehicle operators may be reduced inproportion to the evaluated effectiveness and monitored usage levels ofthe autonomous operation features. For vehicles with autonomouscommunication features that obtain information from external sources(e.g., other vehicles or infrastructure), the risk level and/or pricedetermination may also include an assessment of the availability ofexternal sources of information. Location and/or timing of vehicle usemay thus be monitored and/or weighted to determine the risk associatedwith operation of the vehicle.

Exemplary Autonomous Vehicle Operation System

FIG. 1A illustrates a block diagram of an exemplary autonomous vehicledata system 100 on which the exemplary methods described herein may beimplemented. The high-level architecture includes both hardware andsoftware applications, as well as various data communications channelsfor communicating data between the various hardware and softwarecomponents. The autonomous vehicle data system 100 may be roughlydivided into front-end components 102 and back-end components 104. Thefront-end components 102 may obtain information regarding a vehicle 108(e.g., a car, truck, motorcycle, etc.) and the surrounding environment.An on-board computer 114 may utilize this information to operate thevehicle 108 according to an autonomous operation feature or to assistthe vehicle operator in operating the vehicle 108. To monitor thevehicle 108, the front-end components 102 may include one or moresensors 120 installed within the vehicle 108 that may communicate withthe on-board computer 114. The front-end components 102 may furtherprocess the sensor data using the on-board computer 114 or a mobiledevice 110 (e.g., a smart phone, a tablet computer, a special purposecomputing device, smart watch, wearable electronics, etc.) to determinewhen the vehicle is in operation and information regarding the vehicle.

In some embodiments of the system 100, the front-end components 102 maycommunicate with the back-end components 104 via a network 130. Eitherthe on-board computer 114 or the mobile device 110 may communicate withthe back-end components 104 via the network 130 to allow the back-endcomponents 104 to record information regarding vehicle usage. Theback-end components 104 may use one or more servers 140 to receive datafrom the front-end components 102, store the received data, process thereceived data, and/or communicate information associated with thereceived or processed data.

The front-end components 102 may be disposed within or communicativelyconnected to one or more on-board computers 114, which may bepermanently or removably installed in the vehicle 108. The on-boardcomputer 114 may interface with the one or more sensors 120 within thevehicle 108 (e.g., a digital camera, a LIDAR sensor, an ultrasonicsensor, an infrared sensor, an ignition sensor, an odometer, a systemclock, a speedometer, a tachometer, an accelerometer, a gyroscope, acompass, a geolocation unit, radar unit, etc.), which sensors may alsobe incorporated within or connected to the on-board computer 114.

The front end components 102 may further include a communicationcomponent 122 to transmit information to and receive information fromexternal sources, including other vehicles, infrastructure, or theback-end components 104. In some embodiments, the mobile device 110 maysupplement the functions performed by the on-board computer 114described herein by, for example, sending or receiving information toand from the mobile server 140 via the network 130, such as over one ormore radio frequency links or wireless communication channels. In otherembodiments, the on-board computer 114 may perform all of the functionsof the mobile device 110 described herein, in which case no mobiledevice 110 may be present in the system 100.

Either or both of the mobile device 110 or on-board computer 114 maycommunicate with the network 130 over links 112 and 118, respectively.Either or both of the mobile device 110 or on-board computer 114 may runa Data Application for collecting, generating, processing, analyzing,transmitting, receiving, and/or acting upon data associated with thevehicle 108 (e.g., sensor data, autonomous operation feature settings,or control decisions made by the autonomous operation features) or thevehicle environment (e.g., other vehicles operating near the vehicle108). Additionally, the mobile device 110 and on-board computer 114 maycommunicate with one another directly over link 116.

The mobile device 110 may be either a general-use personal computer,cellular phone, smart phone, tablet computer, smart watch, wearableelectronics, or a dedicated vehicle monitoring or control device.Although only one mobile device 110 is illustrated, it should beunderstood that a plurality of mobile devices 110 may be used in someembodiments. The on-board computer 114 may be a general-use on-boardcomputer capable of performing many functions relating to vehicleoperation or a dedicated computer for autonomous vehicle operation.Further, the on-board computer 114 may be installed by the manufacturerof the vehicle 108 or as an aftermarket modification or addition to thevehicle 108. In some embodiments or under certain conditions, the mobiledevice 110 or on-board computer 114 may function as thin-client devicesthat outsource some or most of the processing to the server 140.

The sensors 120 may be removably or fixedly installed within the vehicle108 and may be disposed in various arrangements to provide informationto the autonomous operation features. Among the sensors 120 may beincluded one or more of a GPS unit, a radar unit, a LIDAR unit, anultrasonic sensor, an infrared sensor, an inductance sensor, a camera,an accelerometer, a tachometer, or a speedometer. Some of the sensors120 (e.g., radar, LIDAR, or camera units) may actively or passively scanthe vehicle environment for obstacles (e.g., other vehicles, buildings,pedestrians, etc.), roadways, lane markings, signs, or signals. Othersensors 120 (e.g., GPS, accelerometer, or tachometer units) may providedata for determining the location or movement of the vehicle 108 (e.g.,via GPS coordinates, dead reckoning, wireless signal triangulation,etc.). Other sensors 120 may be directed to the interior or passengercompartment of the vehicle 108, such as cameras, microphones, pressuresensors, thermometers, or similar sensors to monitor the vehicleoperator and/or passengers within the vehicle 108. Information generatedor received by the sensors 120 may be communicated to the on-boardcomputer 114 or the mobile device 110 for use in autonomous vehicleoperation.

In further embodiments, the front-end components may include aninfrastructure communication device 124 for monitoring the status of oneor more infrastructure components 126. Infrastructure components 126 mayinclude roadways, bridges, traffic signals, gates, switches, crossings,parking lots or garages, toll booths, docks, hangars, or other similarphysical portions of a transportation system's infrastructure. Theinfrastructure communication device 124 may include or becommunicatively connected to one or more sensors (not shown) fordetecting information relating to the condition of the infrastructurecomponent 126. The sensors (not shown) may generate data relating toweather conditions, traffic conditions, or operating status of theinfrastructure component 126.

The infrastructure communication device 124 may be configured to receivethe sensor data generated and determine a condition of theinfrastructure component 126, such as weather conditions, roadintegrity, construction, traffic, available parking spaces, etc. Theinfrastructure communication device 124 may further be configured tocommunicate information to vehicles 108 via the communication component122. In some embodiments, the infrastructure communication device 124may receive information from one or more vehicles 108, while, in otherembodiments, the infrastructure communication device 124 may onlytransmit information to the vehicles 108. The infrastructurecommunication device 124 may be configured to monitor vehicles 108and/or communicate information to other vehicles 108 and/or to mobiledevices 110.

In some embodiments, the communication component 122 may receiveinformation from external sources, such as other vehicles orinfrastructure. The communication component 122 may also sendinformation regarding the vehicle 108 to external sources. To send andreceive information, the communication component 122 may include atransmitter and a receiver designed to operate according topredetermined specifications, such as the dedicated short-rangecommunication (DSRC) channel, wireless telephony, Wi-Fi, or otherexisting or later-developed communications protocols. The receivedinformation may supplement the data received from the sensors 120 toimplement the autonomous operation features. For example, thecommunication component 122 may receive information that an autonomousvehicle ahead of the vehicle 108 is reducing speed, allowing theadjustments in the autonomous operation of the vehicle 108.

In addition to receiving information from the sensors 120, the on-boardcomputer 114 may directly or indirectly control the operation of thevehicle 108 according to various autonomous operation features. Theautonomous operation features may include software applications ormodules implemented by the on-board computer 114 to generate andimplement control commands to control the steering, braking, or throttleof the vehicle 108. To facilitate such control, the on-board computer114 may be communicatively connected to control components of thevehicle 108 by various electrical or electromechanical controlcomponents (not shown). When a control command is generated by theon-board computer 114, it may thus be communicated to the controlcomponents of the vehicle 108 to effect a control action. In embodimentsinvolving fully autonomous vehicles, the vehicle 108 may be operableonly through such control components (not shown). In other embodiments,the control components may be disposed within or supplement othervehicle operator control components (not shown), such as steeringwheels, accelerator or brake pedals, or ignition switches.

In some embodiments, the front-end components 102 communicate with theback-end components 104 via the network 130. The network 130 may be aproprietary network, a secure public internet, a virtual private networkor some other type of network, such as dedicated access lines, plainordinary telephone lines, satellite links, cellular data networks,combinations of these. The network 130 may include one or more radiofrequency communication links, such as wireless communication links 112and 118 with mobile devices 110 and on-board computers 114,respectively. Where the network 130 comprises the Internet, datacommunications may take place over the network 130 via an Internetcommunication protocol.

The back-end components 104 include one or more servers 140. Each server140 may include one or more computer processors adapted and configuredto execute various software applications and components of theautonomous vehicle data system 100, in addition to other softwareapplications. The server 140 may further include a database 146, whichmay be adapted to store data related to the operation of the vehicle 108and its autonomous operation features. Such data might include, forexample, dates and times of vehicle use, duration of vehicle use, useand settings of autonomous operation features, information regardingcontrol decisions or control commands generated by the autonomousoperation features, speed of the vehicle 108, RPM or other tachometerreadings of the vehicle 108, lateral and longitudinal acceleration ofthe vehicle 108, vehicle accidents, incidents or near collisions of thevehicle 108, hazardous or anomalous conditions within the vehicleoperating environment (e.g., construction, accidents, etc.),communication between the autonomous operation features and externalsources, environmental conditions of vehicle operation (e.g., weather,traffic, road condition, etc.), errors or failures of autonomousoperation features, or other data relating to use of the vehicle 108 andthe autonomous operation features, which may be uploaded to the server140 via the network 130. The server 140 may access data stored in thedatabase 146 when executing various functions and tasks associated withthe evaluating feature effectiveness or assessing risk relating to anautonomous vehicle.

Although the autonomous vehicle data system 100 is shown to include onevehicle 108, one mobile device 110, one on-board computer 114, and oneserver 140, it should be understood that different numbers of vehicles108, mobile devices 110, on-board computers 114, and/or servers 140 maybe utilized. For example, the system 100 may include a plurality ofservers 140 and hundreds or thousands of mobile devices 110 or on-boardcomputers 114, all of which may be interconnected via the network 130.Furthermore, the database storage or processing performed by the one ormore servers 140 may be distributed among a plurality of servers 140 inan arrangement known as “cloud computing.” This configuration mayprovide various advantages, such as enabling near real-time uploads anddownloads of information as well as periodic uploads and downloads ofinformation. This may in turn support a thin-client embodiment of themobile device 110 or on-board computer 114 discussed herein.

The server 140 may have a controller 155 that is operatively connectedto the database 146 via a link 156. It should be noted that, while notshown, additional databases may be linked to the controller 155 in aknown manner. For example, separate databases may be used for varioustypes of information, such as autonomous operation feature information,vehicle accidents, road conditions, vehicle insurance policyinformation, or vehicle use information. Additional databases (notshown) may be communicatively connected to the server 140 via thenetwork 130, such as databases maintained by third parties (e.g.,weather, construction, or road network databases). The controller 155may include a program memory 160, a processor 162 (which may be called amicrocontroller or a microprocessor), a random-access memory (RAM) 164,and an input/output (I/O) circuit 166, all of which may beinterconnected via an address/data bus 165. It should be appreciatedthat although only one microprocessor 162 is shown, the controller 155may include multiple microprocessors 162. Similarly, the memory of thecontroller 155 may include multiple RAMs 164 and multiple programmemories 160. Although the I/O circuit 166 is shown as a single block,it should be appreciated that the I/O circuit 166 may include a numberof different types of I/O circuits. The RAM 164 and program memories 160may be implemented as semiconductor memories, magnetically readablememories, or optically readable memories, for example. The controller155 may also be operatively connected to the network 130 via a link 135.

The server 140 may further include a number of software applicationsstored in a program memory 160. The various software applications on theserver 140 may include an autonomous operation information monitoringapplication 141 for receiving information regarding the vehicle 108 andits autonomous operation features (which may include control commands ordecisions of the autonomous operation features), a feature evaluationapplication 142 for determining the effectiveness of autonomousoperation features under various conditions and/or determining operatingcondition of autonomous operation features or components, a risk mappingapplication 143 for determining the risks associated with autonomousoperation feature use along a plurality of road segments associated withan electronic map, a route determination application 144 for determiningroutes suitable for autonomous or semi-autonomous vehicle operation, andan autonomous parking application 145 for assisting in parking andretrieving an autonomous vehicle. The various software applications maybe executed on the same computer processor or on different computerprocessors.

FIG. 1B illustrates a block diagram of an exemplary autonomous vehiclemonitoring system 180 on which the exemplary methods described hereinmay be implemented. In one aspect, system 180 may include a network 130,N number of vehicles 182.1-182.N and respective mobile computing devices184.1-184.N, an external computing device 186, and/or a smartinfrastructure component 188. In one aspect, mobile computing devices184 may be an implementation of mobile computing device 110, whilevehicles 182 may be an implementation of vehicle 108. The vehicles 182may include a plurality of vehicles 108 having autonomous operationfeatures, as well as a plurality of other vehicles not having autonomousoperation features. As illustrated, the vehicle 182.1 may include avehicle controller 181.1, which may be an on-board computer 114 asdiscussed elsewhere herein, while vehicle 182.2 may lack such acomponent. Each of vehicles 182.1 and 182.2 may be configured forwireless inter-vehicle communication, such as vehicle-to-vehicle (V2V)wireless communication and/or data transmission via the communicationcomponent 122, directly via the mobile computing devices 184, orotherwise.

Although system 180 is shown in FIG. 1A as including one network 130,two mobile computing devices 184.1 and 184.2, two vehicles 182.1 and182.2, one external computing device 186, and/or one smartinfrastructure component 188, various embodiments of system 180 mayinclude any suitable number of networks 130, mobile computing devices184, vehicles 182, external computing devices 186, and/or infrastructurecomponents 188. The vehicles 182 included in such embodiments mayinclude any number of vehicles 182.i having vehicle controllers 181.n(such as vehicle 182.1 with vehicle controller 181.1) and vehicles 182.jnot having vehicles controllers (such as vehicle 182.2). Moreover,system 180 may include a plurality of external computing devices 186 andmore than two mobile computing devices 184, any suitable number of whichbeing interconnected directly to one another and/or via network 130.

In one aspect, each of mobile computing devices 184.1 and 184.2 may beconfigured to communicate with one another directly via peer-to-peer(P2P) wireless communication and/or data transfer over a radio link orwireless communication channel. In other aspects, each of mobilecomputing devices 184.1 and 184.2 may be configured to communicateindirectly with one another and/or any suitable device viacommunications over network 130, such as external computing device 186and/or smart infrastructure component 188, for example. In still otheraspects, each of mobile computing devices 184.1 and 184.2 may beconfigured to communicate directly and/or indirectly with other suitabledevices, which may include synchronous or asynchronous communication.

Each of mobile computing devices 184.1 and 184.2 may be configured tosend data to and/or receive data from one another and/or via network 130using one or more suitable communication protocols, which may be thesame communication protocols or different communication protocols. Forexample, mobile computing devices 184.1 and 184.2 may be configured tocommunicate with one another via a direct radio link 183 a, which mayutilize, for example, a Wi-Fi direct protocol, an ad-hoc cellularcommunication protocol, etc. Mobile computing devices 184.1 and 184.2may also be configured to communicate with vehicles 182.1 and 182.2,respectively, utilizing a BLUETOOTH communication protocol (radio linknot shown). In some embodiments, this may include communication betweena mobile computing device 184.1 and a vehicle controller 181.1. In otherembodiments, it may involve communication between a mobile computingdevice 184.2 and a vehicle telephony, entertainment, navigation, orinformation system (not shown) of the vehicle 182.2 that providesfunctionality other than autonomous (or semi-autonomous) vehiclecontrol. Thus, vehicles 182.2 without autonomous operation features maynonetheless be connected to mobile computing devices 184.2 in order tofacilitate communication, information presentation, or similarnon-control operations (e.g., navigation display, hands-free telephony,or music selection and presentation).

To provide additional examples, mobile computing devices 184.1 and 184.2may be configured to communicate with one another via radio links 183 band 183 c by each communicating with network 130 utilizing a cellularcommunication protocol. As an additional example, mobile computingdevices 184.1 and/or 184.2 may be configured to communicate withexternal computing device 186 via radio links 183 b, 183 c, and/or 183e. Still further, one or more of mobile computing devices 184.1 and/or184.2 may also be configured to communicate with one or more smartinfrastructure components 188 directly (e.g., via radio link 183 d)and/or indirectly (e.g., via radio links 183 c and 183 f via network130) using any suitable communication protocols. Similarly, one or morevehicle controllers 181.1 may be configured to communicate directly tothe network 130 (via radio link 183 b) or indirectly through mobilecomputing device 184.1 (via radio link 183 b). Vehicle controllers 181.1may also communicate with other vehicle controllers and/or mobilecomputing devices 184.2 directly or indirectly through mobile computingdevice 184.1 via local radio links 183 a. As discussed elsewhere herein,network 130 may be implemented as a wireless telephony network (e.g.,GSM, CDMA, LTE, etc.), a Wi-Fi network (e.g., via one or more IEEE802.11 Standards), a WiMAX network, a Bluetooth network, etc. Thus,links 183 a-183 f may represent wired links, wireless links, or anysuitable combination thereof. For example, the links 183 e and/or 183 fmay include wired links to the network 130, in addition to, or insteadof, wireless radio connections.

In some embodiments, the external computing device 186 may medicatecommunication between the mobile computing devices 184.1 and 184.2 basedupon location or other factors. In embodiments in which mobile computingdevices 184.1 and 184.2 communicate directly with one another in apeer-to-peer fashion, network 130 may be bypassed and thuscommunications between mobile computing devices 184.1 and 184.2 andexternal computing device 186 may be unnecessary. For example, in someaspects, mobile computing device 184.1 may broadcast geographic locationdata and/or telematics data directly to mobile computing device 184.2.In this case, mobile computing device 184.2 may operate independently ofnetwork 130 to determine operating data, risks associated withoperation, control actions to be taken, and/or alerts to be generated atmobile computing device 184.2 based upon the geographic location data,sensor data, and/or the autonomous operation feature data. In accordancewith such aspects, network 130 and external computing device 186 may beomitted.

However, in other aspects, one or more of mobile computing devices 184.1and/or 184.2 may work in conjunction with external computing device 186to determine operating data, risks associated with operation, controlactions to be taken, and/or alerts to be generated. For example, in someaspects, mobile computing device 184.1 may broadcast geographic locationdata and/or autonomous operation feature data, which is received byexternal computing device 186. In this case, external computing device186 may be configured to determine whether the same or other informationshould be sent to mobile computing device 184.2 based upon thegeographic location data, autonomous operation feature data, or dataderived therefrom.

Mobile computing devices 184.1 and 184.2 may be configured to executeone or more algorithms, programs, applications, etc., to determine ageographic location of each respective mobile computing device (and thustheir associated vehicle) to generate, measure, monitor, and/or collectone or more sensor metrics as telematics data, to broadcast thegeographic data and/or telematics data via their respective radio links,to receive the geographic data and/or telematics data via theirrespective radio links, to determine whether an alert should begenerated based upon the telematics data and/or the geographic locationdata, to generate the one or more alerts, and/or to broadcast one ormore alert notifications. Such functionality may, in some embodiments becontrolled in whole or part by a Data Application operating on themobile computing devices 184, as discussed elsewhere herein. Such DataApplication may communicate between the mobile computing devices 184 andone or more external computing devices 186 (such as servers 140) tofacilitate centralized data collection and/or processing.

In some embodiments, the Data Application may facilitate control of avehicle 182 by a user, such as by selecting vehicle destinations and/orroutes along which the vehicle 182 will travel. The Data Application mayfurther be used to establish restrictions on vehicle use or store userpreferences for vehicle use, such as in a user profile. The user profilemay further include information regarding user skill or risk levels inoperating a vehicle manually or using semi-autonomous operationfeatures, which information may vary by location, time, type ofoperation, environmental conditions, etc. In further embodiments, theData Application may monitor vehicle operation or sensor data inreal-time to make recommendations or for other purposes as describedherein. The Data Application may further facilitate monitoring and/orassessment of the vehicle 182, such as by evaluating operating data todetermine the condition of the vehicle or components thereof (e.g.,sensors, autonomous operation features, etc.).

External computing device 186 may be configured to execute varioussoftware applications, algorithms, and/or other suitable programs.External computing device 186 may be implemented as any suitable type ofdevice to facilitate the functionality as described herein. For example,external computing device 186 may be a server 140 as discuses elsewhereherein. As another example, the external computing device 186 may beanother computing device associated with an operator or owner of avehicle 182, such as a desktop or notebook computer. Althoughillustrated as a single device in FIG. 1B, one or more portions ofexternal computing device 186 may be implemented as one or more storagedevices that are physically co-located with external computing device186, or as one or more storage devices utilizing different storagelocations as a shared database structure (e.g. cloud storage).

In some embodiments, external computing device 186 may be configured toperform any suitable portion of the processing functions remotely thathave been outsourced by one or more of mobile computing devices 184.1and/or 184.2 (and/or vehicle controllers 181.1). For example, mobilecomputing device 184.1 and/or 184.2 may collect data (e.g., geographiclocation data and/or telematics data) as described herein, but may sendthe data to external computing device 186 for remote processing insteadof processing the data locally. In such embodiments, external computingdevice 186 may receive and process the data to determine whether ananomalous condition exists and, if so, whether to send an alertnotification to one or more mobile computing devices 184.1 and 184.2 ortake other actions.

In one aspect, external computing device 186 may additionally oralternatively be part of an insurer computing system (or facilitatecommunications with an insurer computer system), and as such may accessinsurer databases, execute algorithms, execute applications, accessremote servers, communicate with remote processors, etc., as needed toperform insurance-related functions. Such insurance-related functionsmay include assisting insurance customers in evaluating autonomousoperation features, limiting manual vehicle operation based upon risklevels, providing information regarding risk levels associated withautonomous and/or manual vehicle operation along routes, and/ordetermining repair/salvage information for damaged vehicles. Forexample, external computing device 186 may facilitate the receipt ofautonomous operation or other data from one or more mobile computingdevices 184.1-184.N, which may each be running a Data Application toobtain such data from autonomous operation features or sensors 120associated therewith.

In aspects in which external computing device 186 facilitatescommunications with an insurer computing system (or is part of such asystem), data received from one or more mobile computing devices184.1-184.N may include user credentials, which may be verified byexternal computing device 186 or one or more other external computingdevices, servers, etc. These user credentials may be associated with aninsurance profile, which may include, for example, insurance policynumbers, a description and/or listing of insured assets, vehicleidentification numbers of insured vehicles, addresses of insuredstructures, contact information, premium rates, discounts, etc. In thisway, data received from one or more mobile computing devices 184.1-184.Nmay allow external computing device 186 to uniquely identify eachinsured customer and/or whether each identified insurance customer hasinstalled the Data Application. In addition, external computing device186 may facilitate the communication of the updated insurance policies,premiums, rates, discounts, etc., to insurance customers for theirreview, modification, and/or approval—such as via wireless communicationor data transmission to one or more mobile computing devices 184.1-184.Nover one or more radio frequency links or wireless communicationchannels.

In some aspects, external computing device 186 may facilitate indirectcommunications between one or more of mobile computing devices 184,vehicles 182, and/or smart infrastructure component 188 via network 130or another suitable communication network, wireless communicationchannel, and/or wireless link. Smart infrastructure components 188 maybe implemented as any suitable type of traffic infrastructure componentsconfigured to receive communications from and/or to send communicationsto other devices, such as mobile computing devices 184 and/or externalcomputing device 186. Thus, smart infrastructure components 188 mayinclude infrastructure components 126 having infrastructurecommunication devices 124. For example, smart infrastructure component188 may be implemented as a traffic light, a railroad crossing signal, aconstruction notification sign, a roadside display configured to displaymessages, a billboard display, a parking garage monitoring device, etc.

In some embodiments, the smart infrastructure component 188 may includeor be communicatively connected to one or more sensors (not shown) fordetecting information relating to the condition of the smartinfrastructure component 188, which sensors may be connected to or partof the infrastructure communication device 124 of the smartinfrastructure component 188. The sensors (not shown) may generate datarelating to weather conditions, traffic conditions, or operating statusof the smart infrastructure component 188. The smart infrastructurecomponent 188 may be configured to receive the sensor data generated anddetermine a condition of the smart infrastructure component 188, such asweather conditions, road integrity, construction, traffic, availableparking spaces, etc.

In some aspects, smart infrastructure component 188 may be configured tocommunicate with one or more other devices directly and/or indirectly.For example, smart infrastructure component 188 may be configured tocommunicate directly with mobile computing device 184.2 via radio link183 d and/or with mobile computing device 184.1 via links 183 b and 183f utilizing network 130. As another example, smart infrastructurecomponent 188 may communicate with external computing device 186 vialinks 183 e and 183 f utilizing network 130. To provide someillustrative examples of the operation of the smart infrastructurecomponent 188, if smart infrastructure component 188 is implemented as asmart traffic light, smart infrastructure component 188 may change atraffic light from green to red (or vice-versa) or adjust a timing cycleto favor traffic in one direction over another based upon data receivedfrom the vehicles 182. If smart infrastructure component 188 isimplemented as a traffic sign display, smart infrastructure component188 may display a warning message that an anomalous condition (e.g., anaccident) has been detected ahead and/or on a specific roadcorresponding to the geographic location data.

FIG. 2 illustrates a block diagram of an exemplary mobile device 110 oran exemplary on-board computer 114 consistent with the system 100 andthe system 180. The mobile device 110 or on-board computer 114 mayinclude a display 202, a GPS unit 206, a communication unit 220, anaccelerometer 224, one or more additional sensors (not shown), auser-input device (not shown), and/or, like the server 140, a controller204. In some embodiments, the mobile device 110 and on-board computer114 may be integrated into a single device, or either may perform thefunctions of both. The on-board computer 114 (or mobile device 110)interfaces with the sensors 120 to receive information regarding thevehicle 108 and its environment, which information is used by theautonomous operation features to operate the vehicle 108.

Similar to the controller 155, the controller 204 may include a programmemory 208, one or more microcontrollers or microprocessors (MP) 210, aRAM 212, and an I/O circuit 216, all of which are interconnected via anaddress/data bus 214. The program memory 208 includes an operatingsystem 226, a data storage 228, a plurality of software applications230, and/or a plurality of software routines 240. The operating system226, for example, may include one of a plurality of general purpose ormobile platforms, such as the Android™, iOS®, or Windows® systems,developed by Google Inc., Apple Inc., and Microsoft Corporation,respectively. Alternatively, the operating system 226 may be a customoperating system designed for autonomous vehicle operation using theon-board computer 114. The data storage 228 may include data such asuser profiles and preferences, application data for the plurality ofapplications 230, routine data for the plurality of routines 240, andother data related to the autonomous operation features. In someembodiments, the controller 204 may also include, or otherwise becommunicatively connected to, other data storage mechanisms (e.g., oneor more hard disk drives, optical storage drives, solid state storagedevices, etc.) that reside within the vehicle 108.

As discussed with reference to the controller 155, it should beappreciated that although FIG. 2 depicts only one microprocessor 210,the controller 204 may include multiple microprocessors 210. Similarly,the memory of the controller 204 may include multiple RAMs 212 andmultiple program memories 208. Although FIG. 2 depicts the I/O circuit216 as a single block, the I/O circuit 216 may include a number ofdifferent types of I/O circuits. The controller 204 may implement theRAMs 212 and the program memories 208 as semiconductor memories,magnetically readable memories, or optically readable memories, forexample.

The one or more processors 210 may be adapted and configured to executeany of one or more of the plurality of software applications 230 or anyone or more of the plurality of software routines 240 residing in theprogram memory 204, in addition to other software applications. One ofthe plurality of applications 230 may be an autonomous vehicle operationapplication 232 that may be implemented as a series of machine-readableinstructions for performing the various tasks associated withimplementing one or more of the autonomous operation features accordingto the autonomous vehicle operation method 300, described further below.Another of the plurality of applications 230 may be an autonomouscommunication application 234 that may be implemented as a series ofmachine-readable instructions for transmitting and receiving autonomousoperation information to or from external sources via the communicationmodule 220. Still another application of the plurality of applications230 may include an autonomous operation monitoring application 236 thatmay be implemented as a series of machine-readable instructions forsending information regarding autonomous operation of the vehicle to theserver 140 via the network 130. The Data Application for collecting,generating, processing, analyzing, transmitting, receiving, and/oracting upon autonomous operation feature data may also be stored as oneof the plurality of applications 230 in the program memory 208 of themobile computing device 110 or on-board computer 114, which may beexecuted by the one or more processors 210 thereof.

The plurality of software applications 230 may call various of theplurality of software routines 240 to perform functions relating toautonomous vehicle operation, monitoring, or communication. One of theplurality of software routines 240 may be a configuration routine 242 toreceive settings from the vehicle operator to configure the operatingparameters of an autonomous operation feature. Another of the pluralityof software routines 240 may be a sensor control routine 244 to transmitinstructions to a sensor 120 and receive data from the sensor 120. Stillanother of the plurality of software routines 240 may be an autonomouscontrol routine 246 that performs a type of autonomous control, such ascollision avoidance, lane centering, or speed control. In someembodiments, the autonomous vehicle operation application 232 may causea plurality of autonomous control routines 246 to determine controlactions required for autonomous vehicle operation.

Similarly, one of the plurality of software routines 240 may be amonitoring and reporting routine 248 that transmits informationregarding autonomous vehicle operation to the server 140 via the network130. Yet another of the plurality of software routines 240 may be anautonomous communication routine 250 for receiving and transmittinginformation between the vehicle 108 and external sources to improve theeffectiveness of the autonomous operation features. Any of the pluralityof software applications 230 may be designed to operate independently ofthe software applications 230 or in conjunction with the softwareapplications 230.

When implementing the exemplary autonomous vehicle operation method 300,the controller 204 of the on-board computer 114 may implement theautonomous vehicle operation application 232 to communicate with thesensors 120 to receive information regarding the vehicle 108 and itsenvironment and process that information for autonomous operation of thevehicle 108. In some embodiments including external source communicationvia the communication component 122 or the communication unit 220, thecontroller 204 may further implement the autonomous communicationapplication 234 to receive information for external sources, such asother autonomous vehicles, smart infrastructure (e.g., electronicallycommunicating roadways, traffic signals, or parking structures), orother sources of relevant information (e.g., weather, traffic, localamenities). Some external sources of information may be connected to thecontroller 204 via the network 130, such as the server 140 orinternet-connected third-party databases (not shown). Although theautonomous vehicle operation application 232 and the autonomouscommunication application 234 are shown as two separate applications, itshould be understood that the functions of the autonomous operationfeatures may be combined or separated into any number of the softwareapplications 230 or the software routines 240.

When implementing the autonomous operation feature monitoring method400, the controller 204 may further implement the autonomous operationmonitoring application 236 to communicate with the server 140 to provideinformation regarding autonomous vehicle operation. This may includeinformation regarding settings or configurations of autonomous operationfeatures, data from the sensors 120 regarding the vehicle environment,data from the sensors 120 regarding the response of the vehicle 108 toits environment, communications sent or received using the communicationcomponent 122 or the communication unit 220, operating status of theautonomous vehicle operation application 232 and the autonomouscommunication application 234, and/or control commands sent from theon-board computer 114 to the control components (not shown) to operatethe vehicle 108. In some embodiments, control commands generated by theon-board computer 114 but not implemented may also be recorded and/ortransmitted for analysis of how the autonomous operation features wouldhave responded to conditions if the features had been controlling therelevant aspect or aspects of vehicle operation. The information may bereceived and stored by the server 140 implementing the autonomousoperation information monitoring application 141, and the server 140 maythen determine the effectiveness of autonomous operation under variousconditions by implementing the feature evaluation application 142, whichmay include an assessment of autonomous operation featurescompatibility. The effectiveness of autonomous operation features andthe extent of their use may be further used to determine one or morerisk levels associated with operation of the autonomous vehicle by theserver 140.

In addition to connections to the sensors 120 that are external to themobile device 110 or the on-board computer 114, the mobile device 110 orthe on-board computer 114 may include additional sensors 120, such asthe GPS unit 206 or the accelerometer 224, which may provide informationregarding the vehicle 108 for autonomous operation and other purposes.Such sensors 120 may further include one or more sensors of a sensorarray 225, which may include, for example, one or more cameras,accelerometers, gyroscopes, magnetometers, barometers, thermometers,proximity sensors, light sensors, Hall Effect sensors, etc. The one ormore sensors of the sensor array 225 may be positioned to determinetelematics data regarding the speed, force, heading, and/or directionassociated with movements of the vehicle 108. Furthermore, thecommunication unit 220 may communicate with other autonomous vehicles,infrastructure, or other external sources of information to transmit andreceive information relating to autonomous vehicle operation. Thecommunication unit 220 may communicate with the external sources via thenetwork 130 or via any suitable wireless communication protocol network,such as wireless telephony (e.g., GSM, CDMA, LTE, etc.), Wi-Fi (802.11standards), WiMAX, Bluetooth, infrared or radio frequency communication,etc. Furthermore, the communication unit 220 may provide input signalsto the controller 204 via the I/O circuit 216. The communication unit220 may also transmit sensor data, device status information, controlsignals, or other output from the controller 204 to one or more externalsensors within the vehicle 108, mobile devices 110, on-board computers114, or servers 140.

The mobile device 110 or the on-board computer 114 may include auser-input device (not shown) for receiving instructions or informationfrom the vehicle operator, such as settings relating to an autonomousoperation feature. The user-input device (not shown) may include a“soft” keyboard that is displayed on the display 202, an externalhardware keyboard communicating via a wired or a wireless connection(e.g., a Bluetooth keyboard), an external mouse, a microphone, or anyother suitable user-input device. The user-input device (not shown) mayalso include a microphone capable of receiving user voice input.

Data Application

The mobile device 110 and/or on-board computer 114 may run a DataApplication to collect, transmit, receive, and/or process autonomousoperation feature data. Such autonomous operation feature data mayinclude data directly generated by autonomous operation features, suchas control commands used in operating the vehicle 108. Similarly, suchautonomous operation feature data may include shadow control commandsgenerated by the autonomous operation features but not actually used inoperating the vehicle, such as may be generated when the autonomousoperation features are disabled. The autonomous operation feature datamay further include non-control data generated by the autonomousoperation features, such as determinations regarding environmentalconditions in the vehicle operating environment in which the vehicle 108operates (e.g., traffic conditions, construction locations, potholelocations, worn lane markings, corners with obstructed views, etc.). Theenvironmental data may include data or information associated with (i)road construction; (ii) flooded roads; (iii) pot holes; (iv) debris inthe road; (v) road marking visibility; (vi) presence of bicycle lanes;(vii) inoperable traffic lights; (viii) degree of road lighting fromstreet lights; (ix) number of pedestrians nearby; (x) presence of schoolbus stops; (xi) presence of school zones; (xii) traffic directed byemergency personnel; (xiii) traffic accidents; (xiv) detours, and/or(xv) other anomalies. The autonomous operation feature data may yetfurther include sensor data generated by (or derived from sensor datagenerated by) sensors 120 utilized by the autonomous operation features.For example, data from LIDAR and ultrasonic sensors may be used byvehicles for autonomous operation. Such data captures a much moredetailed and complete representation of the conditions in which thevehicle 108 operates than traditional vehicle operation metrics (e.g.,miles driven) or non-autonomous telematics data (e.g., acceleration,position, and time).

Autonomous operation feature data may be processed and used by the DataApplication to determine information regarding the vehicle 108, itsoperation, or its operating environment. The autonomous operationfeature data may further be communicated by the Data Application to aserver 140 via network 130 for processing and/or storage. In someembodiments, the autonomous operation feature data (or informationderived therefrom) may be transmitted directly via radio links 183 orindirectly via network 130 from the vehicle 108 to other vehicles (or tomobile devices 110). By communicating information associated with theautonomous operation feature data to other nearby vehicles, the othervehicles or their operators may make use of such data for routing,control, or other purposes. This may be particularly valuable inproviding detailed information regarding a vehicle environment (e.g.,traffic, accidents, flooding, ice, etc.) collected by a Data Applicationof an autonomous vehicle 108 to a driver of a non-autonomous vehicle viaa Data Application of a mobile device 110 associated with the driver.For example, ice patches may be identified by an autonomous operationfeature of a vehicle controller 181.1 of vehicle 182.1 and transmittedvia the Data Application operating in the mobile computing device 184.1over the network 130 to the mobile computing device 184.2, where awarning regarding the ice patches may be presented to the driver ofvehicle 182.2. As another example, locations of emergency vehicles oraccidents may be determined and communicated between vehicles 182, suchas between an autonomous vehicle 182.1 and a traditional(non-autonomous) vehicle 182.2.

In further embodiments, a Data Application may serve as an interfacebetween the user and an autonomous vehicle 108, via the user's mobiledevice 110 and/or the vehicle's on-board computer 114. The user mayinteract with the Data Application to locate, retrieve, park, control,or monitor the vehicle 108. For example, the Data Application may beused to select a destination and route the vehicle 108 to thedestination, which may include controlling the vehicle to travel to thedestination in a fully autonomous mode. In some embodiments, the DataApplication may further determine and/or provide information regardingthe vehicle 108, such as the operating status or condition of autonomousoperation features, sensors, or other vehicle components (e.g., tirepressure). In yet further embodiments, the Data Application may beconfigured to assess risk levels associated with vehicle operation basedupon location, autonomous operation feature use (including settings),operating conditions, or other factors. Such risk assessment may befurther used in recommending autonomous feature use levels, generatingwarnings to a vehicle operator, or adjusting an insurance policyassociated with the vehicle 108.

Data Applications may be installed and running on a plurality of mobiledevices 110 and/or on-board computers 114 in order to facilitate datasharing and other functions as described herein. Additionally, such DataApplications may provide data to, and receive data from, one or moreservers 140. For example, a Data Application running on a user's mobiledevice 110 may communicate location data to a server 140 via the network130. The server 140 may then process the data to determine a route, risklevel, recommendation, or other action. The server 140 may thencommunicate the determined information to the mobile device 110 and/oron-board computer 114, which may cause the vehicle 108 to operate inaccordance with the determined information (e.g., travel along adetermined optimal route). Thus, the Data Application may facilitatedata communication between the front-end components 102 and the back-endcomponents 104, allowing more efficient processing and data storage.

Data Acquisition

In one aspect, the present embodiments may relate to data acquisition.Data may be gathered via devices employing wireless communicationtechnology, such as Bluetooth or other IEEE communication standards. Inone embodiment, a Bluetooth enabled smartphone or mobile device, and/oran in-dash smart and/or communications device may collect data. The dataassociated with the vehicle, and/or vehicle or driver performance, thatis gathered or collected at, or on, the vehicle may be wirelesslytransmitted to a remote processor or server, such as a remote processoror server associated with an insurance provider. The mobile device 110may receive the data from the on-board computer 114 or the sensors 120,and may transmit the received data to the server 140 via the network130, and the data may be stored in the database 146. In someembodiments, the transmitted data may include real-time sensor data, asummary of the sensor data, processed sensor data, operating data,environmental data, communication data, or a log such data.

Data may be generated by autonomous or semi-autonomous vehicles and/orvehicle mounted sensors (or smart sensors), and then collected byvehicle mounted equipment or processors, including Bluetooth devices,and/or an insurance provider remote processor or server. The datagathered may be used to analyze vehicle decision making. A processor maybe configured to generate data on what an autonomous or semi-autonomousvehicle would have done in a given situation had the driver not takenover manual control/driving of the vehicle or alternative controlactions not taken by the autonomous or semi-autonomous operationfeatures. This type of unimplemented control decision data (related tovehicle decision making) may be useful with respect to analyzinghypothetical situations.

In one embodiment, an application (i.e., the Data Application), or othercomputer or processor instructions, may interact with a vehicle toreceive and/or retrieve data from autonomous or semi-autonomousprocessors and sensors 120. The data retrieved may be related to radar,cameras, sensor output, computer instructions, or application output.Other data related to a smart vehicle controller, car navigation unitinformation (including route history information and typical routestaken), GPS unit information, odometer and/or speedometer information,and smart equipment data may also be gathered or collected. Theapplication and/or other computer instructions may be associated with aninsurance provider remote processor or server.

The control decision data may further include information regardingcontrol decisions generated by one or more autonomous operation featureswithin the vehicle. The operating data and control decision datagathered, collected, and/or acquired may facilitate remote evaluationand/or analysis of what the autonomous or semi-autonomous vehicle was“trying to do” (brake, slow, turn, accelerate, etc.) during operation,as well as what the vehicle actually did do. The data may revealdecisions, and the appropriateness thereof, made by the artificialintelligence or computer instructions associated with one or moreautonomous or semi-autonomous vehicle technologies, functionalities,systems, and/or pieces of equipment. The data may include informationrelated to what the vehicle would have done in a situation if the driverhad not taken over (beginning manual vehicle control) or if theautonomous operation features had been enabled or enabled with differentsettings. Such data may include both the control actions taken by thevehicle and control actions the autonomous or semi-autonomous operationfeatures would have caused the vehicle to take. Thus, in someembodiments, the control decisions data may include informationregarding unimplemented control decisions not implemented by theautonomous operation features to control the vehicle. This may occurwhen an autonomous operation feature generates a control decision orassociated control signal, but the control decision or signal isprevented from controlling the vehicle because the autonomous feature orfunction is disabled, the control decision is overridden by the vehicleoperator, the control signal would conflict with another control signalgenerated by another autonomous operation feature, a more preferredcontrol decision is generated, or an error occurs in the on-boardcomputer 114 or the control system of the vehicle.

For example, a vehicle operator may disable or constrain the operationof some or all autonomous operation features, such as where the vehicleis operated manually or semi-autonomously. The disabled or constrainedautonomous operation features may, however, continue to receive sensordata and generate control decision data that is not implemented.Similarly, one or more autonomous operation features may generate morethan one control decision in a relevant period of time as alternativecontrol decisions. Some of these alternative control decisions may notbe selected by the autonomous operation feature or an autonomousoperation control system to control the vehicle. For example, suchalternative control decisions may be generated based on different setsof sensor or communication data from different sensors 120 or include orexcluding autonomous communication data. As another example, thealternative control decisions may be generated faster than they can beimplemented by the control system of the vehicle, thus preventing allcontrol decisions from being implemented.

In addition to control decision data, other information regarding thevehicle, the vehicle environment, or vehicle operation may be collected,generated, transmitted, received, requested, stored, or recorded inconnection with the control decision data. As discussed elsewhereherein, additional operating data including sensor data from the sensors120, autonomous communication data from the communication component 122or the communication module 220, location data, environmental data, timedata, settings data, configuration data, and/or other relevant data maybe associated with the control decision data. In some embodiments, adatabase or log may store the control decision data and associatedinformation. In further embodiments, the entries in such log or databasemay include a timestamp indicating the date, time, location, vehicleenvironment, vehicle condition, autonomous operation feature settings,and/or autonomous operation feature configuration information associatedwith each entry. Such data may facilitate evaluating the autonomous orsemi-autonomous technology, functionality, system, and/or equipment inhypothetical situations and/or may be used to calculate risk, and inturn adjust insurance policies, premiums, discounts, etc.

The data gathered may be used to evaluate risk associated with theautonomous or semi-autonomous operation feature or technology at issue.As discussed elsewhere herein, information regarding the operation ofthe vehicle may be monitored or associated with test data or actual lossdata regarding losses associated with insurance policies for othervehicles having the autonomous technology or feature to determine risklevels and/or risk profiles. Specifically, the control decision data,sensor data, and other operating data discussed above may be used todetermine risk levels, loss models, and/or risk profiles associated withone or more autonomous or semi-autonomous operation features. Externaldata may further be used to determine risk, as discussed below. Suchdetermined risk levels may further be used to determine insurance rates,premiums, discounts, or costs as discussed in greater detail below.

In one embodiment, the data gathered may be used to determine an averagedistance to another vehicle ahead of, and/or behind, the vehicle duringnormal use of the autonomous or semi-autonomous vehicle technology,functionality, system, and/or equipment. A safe driving distance toother vehicles on the road may lower the risk of accident. The datagathered may also relate to how quickly the technology, functionality,system, and/or equipment may properly stop or slow a vehicle in responseto a light changing from green to yellow, and/or from yellow to red.Timely stopping at traffic lights may also positively impact risk ofcollision. The data gathered may indicate issues not entirely related tothe autonomous or semi-autonomous technology, functionality, system,and/or equipment. For instance, tires spinning and low vehicle speed maybe monitored and identified to determine that vehicle movement was beingaffected by the weather (as compared to the technology, functionality,system, and/or equipment during normal operation). Vehicle tires mayspin with little or no vehicle movement in snow, rain, mud, ice, etc.

The data gathered may indicate a current version of artificialintelligence or computer instructions that the autonomous orsemi-autonomous system or equipment is utilizing. A collision riskfactor may be assigned to each version of computer instructions. Theinsurance provider may then adjust or update insurance policies,premiums, rates, discounts, and/or other insurance-related items basedupon the collision risk factor and/or the artificial intelligence orcomputer instruction versions presently employed by the vehicle (and/orupgrades there to).

The decision and operating data gathered may be merged with outsidedata, such as information related to weather, traffic, construction,and/or other factors, and/or collected from sources besides the vehicle.In some embodiments, such data from outside the vehicle may be combinedwith the control decision data and other operating data discussed aboveto determine risks associated with the operation of one or moreautonomous or semi-autonomous operation features. External dataregarding the vehicle environment may be requested or received via thenetwork 130 and associated with the entries in the log or database basedon the timestamp. For example, the location, date, and time of atimestamp may be used to determine weather and traffic conditions inwhich vehicle operation occurred. Additional external data may includeroad conditions, weather conditions, nearby traffic conditions, type ofroad, construction conditions, presence of pedestrians, presence ofother obstacles, and/or availability of autonomous communications fromexternal sources. For instance, weather may impact certain autonomous orsemi-autonomous technology, functionality, system, and/or equipmentperformance, such as fog, visibility, wind, rain, snow, and/or ice.Certain autonomous or semi-autonomous functionality may have degradedperformance: (1) on ice covered roads; (2) during snow or rain, and/oron snow or rain covered roads; (3) during poor visibility conditions,such as foggy weather; (4) in “stop and go” traffic, such as during rushhour traffic, or slow moving traffic through high construction areas ordowntown areas; and/or (5) caused by other factors.

The system and method may consider the geographical area associated withthe user, or the owner or operator of a vehicle. For instance, rainmitigation functionality or technology for vehicles may be pertinent toreducing the amount of accidents and/or the severity of such accidentsin areas of high rain fall, such as the Pacific Northwest or Florida. Onthe other hand, such functionality may have less of a beneficial impacton accidents or potential accidents in desert locations, such as Nevadaor New Mexico. Construction-related data may also be collected andanalyzed. Construction-related accident avoidance and/or mitigationtechnology, functionality, systems, or associated equipment may be morepertinent in large urban areas involving significant and lengthyconstruction or road connector projects that may include frequentlychanging travel patterns with little notice to drivers.

The data gathered may relate to autonomous vehicle telematics variables.Usage of other technologies and functionalities (including thetechnologies and functionalities discussed elsewhere herein) may bemonitored, and recommended usages thereof (and associated insurancesavings) may be provided to the insured or driver for their reviewand/or approval. Other manners of saving money on existing autoinsurance coverage may be provided to the driver via wirelesscommunication. For instance, a percentage of time that the vehicle is ina (1) “manual” mode or operation; (2) semi-automated, semi-automatic, or“semi-autonomous” mode or operation; and/or (3) fully automated, fullyautomatic, or fully “autonomous” mode or operation may be determinedfrom vehicle sensor data that is remotely collected, such as at or by aninsurance provider remote processor or server.

Also, the data gathered may be used to provide feedback to the customeror insured. For instance, if the vehicle is presently traveling on thehighway, a recommendation or offer may be presented to the driver, suchas via wireless communication with the vehicle that indicates that ifthe driver places the vehicle into autonomous or semi-autonomous drivingmode, the risk of collision may be reduced and/or the driver may bereceive a discount, and/or lower premium on his or her auto insurance.Other manners of potential risk reductions may also be communicated tothe driver or owner of the vehicle. For instance, recommendations and/oradjustments to insurance policies, premiums, rates, discounts, rewards,and/or other insurance-related items may be based upon drivercharacteristics or age, such as beginning or teenage drivers.

The data gathered may originate from various smart parts and/or piecesof smart equipment mounted on a vehicle, including parts configured forwired or wireless communication. For instance, a vehicle may be equippedwith smart brakes; smart tail, head, or turn lights; smart tires; etc.Each piece of smart equipment may have a wired or wireless transmitter.Each piece of smart equipment may be configured to monitor itsoperation, and/or indicate or communicate a warning to the driver whenit is not operating properly. Such smart equipment may be includedwithin the sensors 120.

As an example, when a rear brake light is out, such as from faultyrepair or from normal burn out, that fact may be detected by smartvehicle functionality and the driver may be promptly notified. As aresult, the driver may be able to repair the faulty brake light beforean accident caused by the faulty brake light occurs. In anotherembodiment, the data gathered may also indicate window wipers are notoperating properly, and need to be replaced. The insurance provider mayadjust or update insurance policies, premiums, rates, discounts, and/orother insurance-related items based upon the smart equipment warningfunctionality that may alert drivers of vehicle equipment or vehiclesafety equipment (lights, brakes, etc.) that need to be replaced orrepaired, and thus may reduce collision risk. In addition to addressingliability for collision risk, the technology may also reduce risk oftheft. For instance, stolen vehicles may be tracked via on-board GPSunits and wireless transmitters. Also, the breaking and entering, and/orhot wiring, of vehicles may be more difficult through the use ofanti-hacking measures for smart vehicles or vehicles with electrical orelectronic control systems. The insurance provider may adjust insurancepremiums, rates, and/or other insurance-related items based upon thereduced risk of theft.

Exemplary Autonomous Vehicle Operation Method

FIG. 3 illustrates a flow diagram of an exemplary autonomous vehicleoperation method 300, which may be implemented by the autonomous vehicledata system 100. The method 300 may begin when the controller 204receives a start signal (block 302). The start signal may be a commandfrom the vehicle operator through the user-input device to enable orengage one or more autonomous operation features of the vehicle 108. Insome embodiments, the vehicle operator 108 may further specify settingsor configuration details for the autonomous operation features. Forfully autonomous vehicles, the settings may relate to one or moredestinations, route preferences, fuel efficiency preferences, speedpreferences, or other configurable settings relating to the operation ofthe vehicle 108. In some embodiments, fully autonomous vehicles mayinclude additional features or settings permitting them to operatewithout passengers or vehicle operators within the vehicle. For example,a fully autonomous vehicle may receive an instruction to find a parkingspace within the general vicinity, which the vehicle may do without thevehicle operator. The vehicle may then be returned to a selectedlocation by a request from the vehicle operator via a mobile device 110or otherwise. This feature may further be adapted to return a fullyautonomous vehicle if lost or stolen.

For other autonomous vehicles, the settings may include enabling ordisabling particular autonomous operation features, specifyingthresholds for autonomous operation, specifying warnings or otherinformation to be presented to the vehicle operator, specifyingautonomous communication types to send or receive, specifying conditionsunder which to enable or disable autonomous operation features, orspecifying other constraints on feature operation. For example, avehicle operator may set the maximum speed for an adaptive cruisecontrol feature with automatic lane centering. In some embodiments, thesettings may further include a specification of whether the vehicle 108should be operating as a fully or partially autonomous vehicle.

In embodiments where only one autonomous operation feature is enabled,the start signal may consist of a request to perform a particular task(e.g., autonomous parking) or to enable a particular feature (e.g.,autonomous braking for collision avoidance). In other embodiments, thestart signal may be generated automatically by the controller 204 basedupon predetermined settings (e.g., when the vehicle 108 exceeds acertain speed or is operating in low-light conditions). In someembodiments, the controller 204 may generate a start signal whencommunication from an external source is received (e.g., when thevehicle 108 is on a smart highway or near another autonomous vehicle).In some embodiments, the start signal may be generated by or received bythe Data Application running on a mobile device 110 or on-board computer114 within the vehicle 108. The Data Application may further set orrecord settings for one or more autonomous operation features of thevehicle 108.

After receiving the start signal at block 302, the controller 204receives sensor data from the sensors 120 during vehicle operation(block 304). In some embodiments, the controller 204 may also receiveinformation from external sources through the communication component122 or the communication unit 220. The sensor data may be stored in theRAM 212 for use by the autonomous vehicle operation application 232. Insome embodiments, the sensor data may be recorded in the data storage228 or transmitted to the server 140 via the network 130. The DataApplication may receive the sensor data, or a portion thereof, and storeor transmit the received sensor data. In some embodiments, the DataApplication may process or determine summary information from the sensordata before storing or transmitting the summary information. The sensordata may alternately either be received by the controller 204 as rawdata measurements from one of the sensors 120 or may be preprocessed bythe sensor 120 prior to being received by the controller 204. Forexample, a tachometer reading may be received as raw data or may bepreprocessed to indicate vehicle movement or position. As anotherexample, a sensor 120 comprising a radar or LIDAR unit may include aprocessor to preprocess the measured signals and send data representingdetected objects in 3-dimensional space to the controller 204.

The autonomous vehicle operation application 232 or other applications230 or routines 240 may cause the controller 204 to process the receivedsensor data in accordance with the autonomous operation features (block306). The controller 204 may process the sensor data to determinewhether an autonomous control action is required or to determineadjustments to the controls of the vehicle 108 (i.e., control commands).For example, the controller 204 may receive sensor data indicating adecreasing distance to a nearby object in the vehicle's path and processthe received sensor data to determine whether to begin braking (and, ifso, how abruptly to slow the vehicle 108). As another example, thecontroller 204 may process the sensor data to determine whether thevehicle 108 is remaining with its intended path (e.g., within lanes on aroadway). If the vehicle 108 is beginning to drift or slide (e.g., as onice or water), the controller 204 may determine appropriate adjustmentsto the controls of the vehicle to maintain the desired bearing. If thevehicle 108 is moving within the desired path, the controller 204 maynonetheless determine whether adjustments are required to continuefollowing the desired route (e.g., following a winding road). Under someconditions, the controller 204 may determine to maintain the controlsbased upon the sensor data (e.g., when holding a steady speed on astraight road).

In some embodiments, the Data Application may record information relatedto the processed sensor data, including whether the autonomous operationfeatures have determined one or more control actions to control thevehicle and/or details regarding such control actions. The DataApplication may record such information even when no control actions aredetermined to be necessary or where such control actions are notimplemented. Such information may include information regarding thevehicle operating environment determined from the processed sensor data(e.g., construction, other vehicles, pedestrians, anomalousenvironmental conditions, etc.). The information collected by the DataApplication may further include an indication of whether and/or how thecontrol actions are implemented using control components of the vehicle108.

When the controller 204 determines an autonomous control action isrequired (block 308), the controller 204 may cause the controlcomponents of the vehicle 108 to adjust the operating controls of thevehicle to achieve desired operation (block 310). For example, thecontroller 204 may send a signal to open or close the throttle of thevehicle 108 to achieve a desired speed. Alternatively, the controller204 may control the steering of the vehicle 108 to adjust the directionof movement. In some embodiments, the vehicle 108 may transmit a messageor indication of a change in velocity or position using thecommunication component 122 or the communication module 220, whichsignal may be used by other autonomous vehicles to adjust theircontrols. As discussed elsewhere herein, the controller 204 may also logor transmit the autonomous control actions to the server 140 via thenetwork 130 for analysis. In some embodiments, an application (which maybe a Data Application) executed by the controller 204 may communicatedata to the server 140 via the network 130 or may communicate such datato the mobile device 110 for further processing, storage, transmissionto nearby vehicles or infrastructure, and/or communication to the server140 via network 130.

The controller 204 may continue to receive and process sensor data atblocks 304 and 306 until an end signal is received by the controller 204(block 312). The end signal may be automatically generated by thecontroller 204 upon the occurrence of certain criteria (e.g., thedestination is reached or environmental conditions require manualoperation of the vehicle 108 by the vehicle operator). Alternatively,the vehicle operator may pause, terminate, or disable the autonomousoperation feature or features using the user-input device or by manuallyoperating the vehicle's controls, such as by depressing a pedal orturning a steering instrument. When the autonomous operation featuresare disabled or terminated, the controller 204 may either continuevehicle operation without the autonomous features or may shut off thevehicle 108, depending upon the circumstances.

Where control of the vehicle 108 must be returned to the vehicleoperator, the controller 204 may alert the vehicle operator in advanceof returning to manual operation. The alert may include a visual, audio,or other indication to obtain the attention of the vehicle operator. Insome embodiments, the controller 204 may further determine whether thevehicle operator is capable of resuming manual operation beforeterminating autonomous operation. If the vehicle operator is determinednot to be capable of resuming operation, the controller 204 may causethe vehicle to stop or take other appropriate action.

To control the vehicle 108, the autonomous operation features maygenerate and implement control decisions relating to the control of themotive, steering, and stopping components of the vehicle 108. Thecontrol decisions may include or be related to control commands issuedby the autonomous operation features to control such control componentsof the vehicle 108 during operation. In some embodiments, controldecisions may include decisions determined by the autonomous operationfeatures regarding control commands such feature would have issued underthe conditions then occurring, but which control commands were notissued or implemented. For example, an autonomous operation feature maygenerate and record shadow control decisions it would have implementedif engaged to operate the vehicle 108 even when the feature isdisengaged (or engaged using other settings from those that wouldproduce the shadow control decisions).

Data regarding the control decisions actually implemented and/or theshadow control decisions not implemented to control the vehicle 108 maybe recorded for use in assessing autonomous operation featureeffectiveness, accident reconstruction and fault determination, featureuse or settings recommendations, risk determination and insurance policyadjustments, or other purposes as described elsewhere herein. Forexample, actual control decisions may be compared against controldecisions that would have been made by other systems, software versions,or with additional sensor data or communication data.

As used herein, the terms “preferred” or “preferably made” controldecisions mean control decisions that optimize some metric associatedwith risk under relevant conditions. Such metric may include, amongother things, a statistical correlation with one or more risks (e.g.,risks related to a vehicle collision) or an expected value associatedwith risks (e.g., a risk-weighted expected loss associated withpotential vehicle accidents). The preferably made, or preferred orrecommended, control decisions discussed herein may include controldecisions or control decision outcomes that are less risky, have lowerrisk or the lowest risk of all the possible or potential controldecisions given various operating conditions, and/or are otherwiseideal, recommended, or preferred based upon various operatingconditions, including autonomous system or feature capability; currentroad, environmental or weather, traffic, or construction conditionsthrough which the vehicle is traveling; and/or current versions ofautonomous system software or components that the autonomous vehicle isequipped with and using.

The preferred or recommended control decisions may result in the lowestlevel of potential or actual risk of all the potential or possiblecontrol decisions given a set of various operating conditions and/orsystem features or capabilities. Alternatively, the preferred orrecommended control decisions may result in a lower level of potentialor actual risk (for a given set of operating conditions) to theautonomous vehicle and passengers, and other people or vehicles, thansome of the other potential or possible control decisions that couldhave been made by the autonomous system or feature.

Exemplary Monitoring Method

FIG. 4A is a flow diagram depicting an exemplary autonomous vehicleoperation monitoring method 400, which may be implemented by theautonomous vehicle data system 100. The method 400 monitors theoperation of the vehicle 108 and transmits information regarding thevehicle 108 to the server 140, which information may then be used todetermine autonomous operation feature usage or effectiveness. Themethod 400 may be used for monitoring the state of the vehicle 108, forproviding data to other vehicles 182, for responding to emergencies orunusual situations during vehicle use, for testing autonomous operationfeatures in a controlled environment, for determining actual feature useduring vehicle operation outside a test environment, for assessment offeature operation, and/or for other purposes described herein. Inalternative embodiments, the method 400 may be implemented whenever thevehicle 108 is in operation (manual or autonomous) or only when theautonomous operation features are enabled. The method 400 may likewisebe implemented as either a real-time process, in which informationregarding the vehicle 108 is communicated to the server 140 whilemonitoring is ongoing, or as a periodic process, in which theinformation is stored within the vehicle 108 and communicated to theserver 140 at intervals (e.g., upon completion of a trip or when anincident occurs). In some embodiments, the method 400 may communicatewith the server 140 in real-time when certain conditions exist (e.g.,when a sufficient data connection through the network 130 exists or whenno roaming charges would be incurred). In further embodiments, a DataApplication executed by the mobile device 110 and/or on-board computer114 may perform such monitoring, recording, and/or communicationfunctions, including any of the functions described below with respectto blocks 402-434.

The method 400 may begin when the controller 204 receives an indicationof vehicle operation (block 402). The indication may be generated whenthe vehicle 108 is started or when an autonomous operation feature isenabled by the controller 204 or by input from the vehicle operator, asdiscussed above. In response to receiving the indication, the controller204 may create a timestamp (block 404). The timestamp may includeinformation regarding the date, time, location, vehicle environment,vehicle condition, and autonomous operation feature settings orconfiguration information. The date and time may be used to identify onevehicle trip or one period of autonomous operation feature use, inaddition to indicating risk levels due to traffic or other factors. Theadditional location and environmental data may include informationregarding the position of the vehicle 108 from the GPS unit 206 and itssurrounding environment (e.g., road conditions, weather conditions,nearby traffic conditions, type of road, construction conditions,presence of pedestrians, presence of other obstacles, availability ofautonomous communications from external sources, etc.). Vehiclecondition information may include information regarding the type, make,and model of the vehicle 108, the age or mileage of the vehicle 108, thestatus of vehicle equipment (e.g., tire pressure, non-functioninglights, fluid levels, etc.), or other information relating to thevehicle 108. In some embodiments, vehicle condition information mayfurther include information regarding the sensors 120, such as type,configuration, or operational status (which may be determined, forexample, from analysis of actual or test data from the sensors). In someembodiments, the timestamp may be recorded on the client device 114, themobile device 110, or the server 140.

The autonomous operation feature settings may correspond to informationregarding the autonomous operation features, such as those describedabove with reference to the autonomous vehicle operation method 300. Theautonomous operation feature configuration information may correspond toinformation regarding the number and type of the sensors 120 (which mayinclude indications of manufacturers and models of the sensors 120), thedisposition of the sensors 120 within the vehicle 108 (which may includedisposition of sensors 120 within one or more mobile devices 110), theone or more autonomous operation features (e.g., the autonomous vehicleoperation application 232 or the software routines 240), autonomousoperation feature control software, versions of the softwareapplications 230 or routines 240 implementing the autonomous operationfeatures, or other related information regarding the autonomousoperation features.

For example, the configuration information may include the make andmodel of the vehicle 108 (indicating installed sensors 120 and the typeof on-board computer 114), an indication of a malfunctioning or obscuredsensor 120 in part of the vehicle 108, information regarding additionalafter-market sensors 120 installed within the vehicle 108, a softwareprogram type and version for a control program installed as anapplication 230 on the on-board computer 114, and software program typesand versions for each of a plurality of autonomous operation featuresinstalled as applications 230 or routines 240 in the program memory 208of the on-board computer 114.

During operation, the sensors 120 may generate sensor data regarding thevehicle 108 and its environment, which may include other vehicles 182within the operating environment of the vehicle 108. In someembodiments, one or more of the sensors 120 may preprocess themeasurements and communicate the resulting processed data to theon-board computer 114 and/or the mobile device 110. The controller 204may receive sensor data from the sensors 120 (block 406). The sensordata may include information regarding the vehicle's position, speed,acceleration, direction, and responsiveness to controls. The sensor datamay further include information regarding the location and movement ofobstacles or obstructions (e.g., other vehicles, buildings, barriers,pedestrians, animals, trees, or gates), weather conditions (e.g.,precipitation, wind, visibility, or temperature), road conditions (e.g.,lane markings, potholes, road material, traction, or slope), signs orsignals (e.g., traffic signals, construction signs, building signs ornumbers, or control gates), or other information relating to thevehicle's environment. In some embodiments, sensors 120 may indicate thenumber of passengers within the vehicle 108, including an indication ofwhether the vehicle is entirely empty.

In addition to receiving sensor data from the sensors 120, in someembodiments the controller 204 may receive autonomous communication datafrom the communication component 122 or the communication module 220(block 408). The communication data may include information from otherautonomous vehicles (e.g., sudden changes to vehicle speed or direction,intended vehicle paths, hard braking, vehicle failures, collisions, ormaneuvering or stopping capabilities), infrastructure (road or laneboundaries, bridges, traffic signals, control gates, or emergencystopping areas), or other external sources (e.g., map databases, weatherdatabases, or traffic and accident databases). In some embodiments, thecommunication data may include data from non-autonomous vehicles, whichmay include data regarding vehicle operation or anomalies within theoperating environment determined by a Data Application operating on amobile device 110 or on-board computer 114. The communication data maybe combined with the received sensor data received to obtain a morerobust understanding of the vehicle environment. For example, the server140 or the controller 204 may combine sensor data indicating frequentchanges in speed relative to tachometric data with map data relating toa road upon which the vehicle 108 is traveling to determine that thevehicle 108 is in an area of hilly terrain. As another example, weatherdata indicating recent snowfall in the vicinity of the vehicle 108 maybe combined with sensor data indicating frequent slipping or lowtraction to determine that the vehicle 108 is traveling on asnow-covered or icy road.

The controller 204 may process the sensor data, the communication data,and the settings or configuration information to determine whether anincident has occurred (block 410). As used herein, an “incident” is anoccurrence during operation of an autonomous vehicle outside of normalsafe operating conditions, such that one or more of the followingoccurs: (i) there is an interruption of ordinary vehicle operation, (ii)there is damage to the vehicle or other property, (iii) there is injuryto a person, (iv) the conditions require action to be taken by a vehicleoperator, autonomous operation feature, pedestrian, or other party toavoid damage or injury, and/or (v) an anomalous condition is detectedthat requires an adjustment to or outside of ordinary vehicle operation.Incidents may include collisions, hard braking, hard acceleration,evasive maneuvering, loss of traction, detection of objects within athreshold distance from the vehicle 108, alerts presented to the vehicleoperator, component failure, inconsistent readings from sensors 120, orattempted unauthorized access to the on-board computer by externalsources. Incidents may also include accidents, vehicle breakdowns, flattires, empty fuel tanks, or medical emergencies. Incidents may furtherinclude identification of construction requiring the vehicle to detouror stop, hazardous conditions (e.g., fog or road ice), or otheranomalous environmental conditions.

In some embodiments, the controller 204 may anticipate or project anexpected incident based upon sensor or external data, allowing thecontroller 204 to send control signals to minimize the negative effectsof the incident. For example, the controller 204 may cause the vehicle108 to slow and move to the shoulder of a road immediately beforerunning out of fuel. As another example, adjustable seats within thevehicle 108 may be adjusted to better position vehicle occupants inanticipation of a collision, windows may be opened or closed, or airbagsmay be deployed.

When an incident is determined to have occurred (block 412), informationregarding the incident and the vehicle status may be recorded (block414), either in the data storage 228 or the database 146. Theinformation recorded may include sensor data, communication data, andsettings or configuration information prior to, during, and immediatelyfollowing the incident. In some embodiments, a preliminary determinationof fault may also be produced and stored. The information may furtherinclude a determination of whether the vehicle 108 has continuedoperating (either autonomously or manually) or whether the vehicle 108is capable of continuing to operate in compliance with applicable safetyand legal requirements. If the controller 204 determines that thevehicle 108 has discontinued operation or is unable to continueoperation (block 416), the method 400 may terminate. If the vehicle 108continues operation, then the method 400 may continue as described belowwith reference to block 418.

FIG. 4B illustrates an alternative portion of the method 400 followingan incident. When an incident is determined to have occurred (block412), the controller 204 or the server 140 may record status andoperating information (block 414), as above. In some instances, theincident may interrupt communication between the vehicle 108 and theserver 140 via network 130, such that not all information typicallyrecorded will be available for recordation and analysis by the server140. Based upon the recorded data, the server 140 or the controller 204may determine whether assistance may be needed at the location of thevehicle 108 (block 430). For example, the controller may determine thata head-on collision has occurred based upon sensor data (e.g., airbagdeployment, automatic motor shut-off, LIDAR data indicating a collision,etc.) and may further determine based upon information regarding thespeed of the vehicle 108 and other information that medical, police,and/or towing services will be necessary. The determination thatassistance is needed may further include a determination of types ofassistance needed (e.g., police, ambulance, fire, towing, vehiclemaintenance, fuel delivery, etc.). This determination may includeanalysis of the type of incident, the sensor data regarding the incident(e.g., images from outward facing or inward facing cameras installedwithin the vehicle, identification of whether any passengers werepresent within the vehicle, determination of whether any pedestrians orpassengers in other vehicles were involved in the incident, etc.). Thedetermination of whether assistance is needed may further includeinformation regarding the determined status of the vehicle 108.

In some embodiments, the determination regarding whether assistance isneeded may be supplemented by a verification attempt, such as a phonecall or communication through the on-board computer 114. Where theverification attempt indicates assistance is required or communicationattempts fail, the server 140 or controller 204 would then determinethat assistance is needed, as described above. For example, whenassistance is determined to be needed following an accident involvingthe vehicle 108, the server 140 may direct an automatic telephone callto a mobile telephone number associated with the vehicle 108 or thevehicle operator. If no response is received, or if the respondentindicates assistance is required, the server 140 may proceed to cause arequest for assistance to be generated.

When assistance is determined to be needed (block 432), the controller204 or the server 140 may send a request for assistance (block 434). Therequest may include information regarding the vehicle 108, such as thevehicle's location, the type of assistance required, other vehiclesinvolved in the incident, pedestrians involved in the incident, vehicleoperators or passengers involved in the incident, and/or other relevantinformation. The request for assistance may include telephonic, data, orother requests to one or more emergency or vehicular service providers(e.g., local police, fire departments, state highway patrols, emergencymedical services, public or private ambulance services, hospitals,towing companies, roadside assistance services, vehicle rental services,local claims representative offices, etc.). After sending a request forassistance (block 434) or when assistance is determined not to be needed(block 432), the controller 204 or the server 140 may next determinewhether the vehicle is operational (block 416), as described above. Themethod 400 may then end or continue as indicated in FIG. 4A.

In some embodiments, the controller 204 may further determineinformation regarding the likely cause of a collision or other incident.Alternatively, or additionally, the server 140 may receive informationregarding an incident from the on-board computer 114 and determinerelevant additional information regarding the incident from the sensordata. For example, the sensor data may be used to determine the pointsof impact on the vehicle 108 and another vehicle involved in acollision, the relative velocities of each vehicle, the road conditionsat the time of the incident, and the likely cause or the party likely atfault. This information may be used to determine risk levels associatedwith autonomous vehicle operation, as described below, even where theincident is not reported to the insurer.

The controller 204 may determine whether a change or adjustment to oneor more of the settings or configuration of the autonomous operationfeatures has occurred (block 418). Changes to the settings may includeenabling or disabling an autonomous operation feature or adjusting thefeature's parameters (e.g., resetting the speed on an adaptive cruisecontrol feature). For example, a vehicle operator may selectively enableor disable autonomous operation features such as automatic braking, lanecentering, or even fully autonomous operation at different times. If thesettings or configuration are determined to have changed, the newsettings or configuration may be recorded (block 422), either in thedata storage 228 or the database 146. For example, the Data Applicationmay log autonomous operation feature use and changes in a log file,including timestamps associated with the features in use.

Next, the controller 204 may record the operating data relating to thevehicle 108 in the data storage 228 or communicate the operating data tothe server 140 via the network 130 for recordation in the database 146(block 424). The operating data may include the settings orconfiguration information, the sensor data, and/or the communicationdata discussed above. In some embodiments, operating data related tonormal autonomous operation of the vehicle 108 may be recorded. In otherembodiments, only operating data related to incidents of interest may berecorded, and operating data related to normal operation may not berecorded. In still other embodiments, operating data may be stored inthe data storage 228 until a sufficient connection to the network 130 isestablished, but some or all types of incident information may betransmitted to the server 140 using any available connection via thenetwork 130.

The controller 204 may then determine whether operation of the vehicle108 remains ongoing (block 426). In some embodiments, the method 400 mayterminate when all autonomous operation features are disabled, in whichcase the controller 204 may determine whether any autonomous operationfeatures remain enabled. When the vehicle 108 is determined to beoperating (or operating with at least one autonomous operation featureenabled), the method 400 may continue through blocks 406-426 untilvehicle operation has ended. When the vehicle 108 is determined to haveceased operating (or is operating without autonomous operation featuresenabled), the controller 204 may record the completion of operation(block 428), either in the data storage 228 or the database 146. In someembodiments, a second timestamp corresponding to the completion ofvehicle operation may likewise be recorded, as above.

Exemplary Methods of Mapping Suitability of Autonomous Operation

FIG. 5 illustrates a flow diagram of an exemplary autonomous operationsuitability mapping method 500 for determining the suitability ofvarious locations for autonomous and/or semi-autonomous operation ofvehicles. The method 500 may be used to obtain and process data frommultiple sources to determine suitability of locations such as roadsegments for various degrees of autonomous or semi-autonomous vehicleoperation. For example, operating data from a plurality of autonomousvehicles may be used to determine whether each of a plurality of roadsegments may be safely traversed by vehicles using particular autonomousoperation features or technologies. Such plurality of vehicles mayinclude a fleet of vehicles commonly owned, operated, or controlled orotherwise operated in a coordinated manner by one or more parties (e.g.,a fleet of taxi cabs, delivery vehicles, etc.). This informationregarding whether autonomous vehicles may safely operate in variousautonomous or semi-autonomous modes along particular roadways mayfurther be used to establish permissions or recommendations regardingthe roadways for autonomous operation feature use by other vehicles. Forexample, a control system of an autonomous vehicle 108 may not allow(e.g., may disable) aspects of autonomous or semi-autonomous operationalong road segments rated below a minimum threshold safety level for theuse of relevant autonomous operation features.

The method 500 may begin by receiving operating data from a plurality ofautonomous vehicles (block 502) and map data including a plurality ofroad segments from a map database (block 504). The operating data may beassociated with the road segments based upon GPS or other locationindications of the operating data (block 506). The method 500 may thenprocess the operating data to analyze each of a number of road segments.A road segment may be identified for analysis (block 508), and risksassociated with a level of autonomous or semi-autonomous operation onthe road segment may be determined (block 510). From suchdeterminations, one or more autonomous operation scores may becalculated for the road segment (block 512) and stored for further use(block 514). The method 500 may then check whether additional roadsegments remain to be analyzed (block 516). When no further roadsegments remain to be analyzed, the method 500 may (in some embodiments)generate an electronic map based upon the calculated scores for the roadsegments (block 518). Generating the electronic map may includegenerating graphical map tiles, overlay tiles in a map database, or dataentries in a map database to store the electronic map data for furtheruse in generating a visible map or for autonomous vehicle navigation.The generated electronic map (or portions thereof) may be displayed orpresented to a user to aid in vehicle operation or route selection, insome embodiments.

At block 502, an external computing device 186 (such as a server 140)may receive operating data from a plurality of autonomous orsemi-autonomous vehicles 182 (such as the vehicle 108). The operatingdata may be received via a Data Application running on a mobile device110 and/or on-board computer 114. In some embodiments, operating datamay be received from both autonomous and semi-autonomous vehicles 182.In further embodiments, this data may be supplemented with data fromadditional sources. Such additional sources may include databases ofroad or other environmental conditions (e.g., weather conditions,construction zones, traffic levels, estimated travel times, etc.),databases of vehicle collisions (e.g., insurance claims, insurancelosses, police reports, etc.), or other databases of relevantinformation. For example, the additional data may include data regardingvehicle accidents, collisions, or other loss events obtained from adatabase maintained by an insurer or a governmental agency. In someembodiments, further data may include information regarding otherhazardous events, regardless of whether a loss was incurred. Suchhazardous events may include not only accidents and other events causingdamage, but also occurrences of loss of control, hard braking oracceleration (i.e., beyond a threshold level of force in the directionof travel), hard swerving (i.e., beyond a threshold level of force in adirection perpendicular to the direction of travel), or near collisions(i.e., times when a vehicle came within an unsafe distance of anotherobject). Regardless of the source, the data received may be associatedwith geographic locations. Such associations may be indicated bygeospatial coordinates (e.g., GPS position), relative location data(e.g., street addresses, intersections, etc.), or area indications(e.g., cities, counties, types of roads, etc.).

At block 504, the external computing device 186 (such as a server 140)may similarly receive map data indicating a plurality of known roadsegments. The map data may be obtained upon requesting such data from amap database storing roadway data. For example, a map database mayinclude a plurality (frequently thousands or millions, depending uponthe geographic scope of the database) of line segments indicated bygeopositioning coordinates of the endpoints of the segments. The roadsegments may individually include only portions of a stretch of roadway(e.g., a block, a quarter mile, etc.), which interconnect to form arepresentation of a roadway system or network. In some embodiments, suchmap data may be obtained from a third party as a copy of a database orvia access through an Application Program Interface (API). The map data(and the operating data discussed above) may be received for a limitedgeographic area for which road segments are to be evaluated.

At block 506, the external computing device 186 (such as a server 140)may associate the received operating data with the road segments in thereceived map data. This may include converting one or both types of thereceived data (viz., the operating data and the map data) to a commonlocation identification system. For example, part of the operating datamay include street addresses or intersections, which may be convertedinto GPS coordinates for matching with the road segment data. In someembodiments, some road segments may be grouped or combined into relevantsegments. For example, several segments of a long and winding roadbetween intersections may be combined to facilitate more efficientanalysis because visual mapping of the road segments may be irrelevantto the evaluation. The road segment data and the operating data mayfurther be associated by a cross-reference table, by merging the data,or using other known data management techniques. In some embodiments,the operating data may not be associated with the road segments untileach relevant road segment is selected for analysis, which may be moreefficient when a small number of road segments are to be rated.

Once the operating data has been associated with the map data, one ormore road segments may be analyzed to determine risks associated withautonomous or semi-autonomous operation thereupon. Blocks 508-516 may berepeated in a loop until all road segments (or all road segment selectedfor analysis) have been analyzed and scored. In some embodiments, notall road segments in the received map data will be analyzed. Forexample, road segments for which no corresponding operating has beenreceived may not be analyzed. Similarly road segments for which toolittle operating data has been received (e.g., less than a thresholdnumber of independent data points, less than a threshold number ofseparate vehicle trips associated with the road segment, etc.) may notbe analyzed. In some such embodiments, such unanalyzed road segments maynonetheless receive a default score or flag indicative of theirunanalyzed status. In other embodiments, such as where the method 500 isused to update existing autonomous operation suitability map data, suchunanalyzed road segments may retain their previously assigned score andother data. As another example, a subset of the received road segmentsmay be selected for analysis, either by a user or automatically. A usermay select a group of road segments to analyze or may selectcharacteristics of road segments to generate a group (e.g., by selectingroad segments within a geographic area, highway road segments, urbanarea road segments, etc.). Alternatively, a group of road segments maybe automatically identified for analysis upon the occurrence of anevent, such as a request from a vehicle 108 for data near the vehicle'scurrent position or along a route.

At block 508, the external computing device 186 (such as a server 140)may identify a particular road segment from the map data to analyze. Theroad segment may be identified by its position in a list of roadsegments, which may be sorted or unsorted. In some embodiments, an indexor counter may be used to indicate the next road segment to be analyzed.When the road segment is identified, the operating data and any otherdata associated with the road segment may be accessed, copied, or movedinto volatile memory to facilitate analysis.

At block 510, the external computing device 186 (such as a server 140)may determine one or more risk levels associated with the road segment.Machine learning techniques (e.g., support vectors, neural networks,random forests, naïve Bayesian classifiers, etc.) may be used toidentify or estimate the magnitude of salient risk factors associatedwith autonomous operation feature use on the road segment. Such riskfactors may include time of day, weather conditions, traffic conditions,speed, type of vehicle, types of sensors used by the vehicle, types ofautonomous operation features in use, versions of autonomous operationfeatures, interactions between autonomous operation features, autonomousoperation feature settings or configurations, driver behavior, or othersimilar factors that may be derived from the data. Alternatively,statistical regression using a set of predetermined models may be usedto estimate the effects of selected risk factors determinable from thedata. In either case, the external computing device 186 may use thedetermined effects of the risk factors to further determine one or morerisks associated with autonomous or semi-autonomous vehicle operation onthe road segment.

The one or more risk levels may include summary levels associated withgroupings of combinations of risk factors, such as fully autonomousoperation or semi-autonomous operation in which the driver activelysteers the vehicle. In some embodiments, a risk level may be determinedfor each autonomous operation feature or category of autonomousoperation features (which risk level may ignore or assume a defaulteffect of interactions between autonomous operation features). Infurther embodiments, average risk levels for the road segment may bedetermined for a small number of categories of general levels ofautonomous operation, such as the NHTSA's five categories of vehicleautomation (ranging from category 0 with no autonomous operation throughcategory 4 with fully autonomous operation). Of course, the quantity ofoperating data available for the road segment will affect the level ofdetail at which risk levels may be determined, both in terms ofspecificity of the risk levels and the number of separate risk levelsdetermined for the road segment. In a preferred embodiment, operatingdata from a large number of vehicle trips along the road segment (i.e.,hundreds or thousands of separate vehicle trips by at least severaltypes of autonomous vehicles using different types and settings ofautonomous operation features) may be used to determine risk levelsassociated with a plurality of autonomous operation feature use levels,configurations, and settings for a plurality of types of autonomousvehicles in various environmental conditions.

At block 512, the external computing device 186 (such as a server 140)may calculate one or more scores for autonomous or semi-autonomousoperation associated with the road segment (i.e., suitability scores).This may include determining a score representing a risk level category(e.g., a score of 5 indicating high risk, a score of 4 indicatingmedium-high risk, a score of 1 indicating low risk, a score of 0indicating that the road segment has not been analyzed, etc.) based upona risk level determined as discussed above. The score may similarlyrepresent a maximum recommended (or permitted) level of autonomousoperation feature use on the road segment, which may depend uponenvironmental conditions or other factors as discussed above. In someembodiments, the score may be constrained by a statutory proscriptionregarding levels or types of autonomous or semi-autonomous vehiclefeature use on the road segment (e.g., limitations on fully autonomousoperation in certain locations), information regarding which may beobtained from one or more servers associated with government agencies orother sources. Thus, the scores may indicate recommended or allowedautonomous operation feature usage or usage levels for road segments orareas.

In further embodiments, the score may indicate an adjustment factor foran insurance policy metric, such as a premium or deductible. Forexample, a high-risk usage profile along the road segment may beassociated with an adjustment factor greater than one (indicating anincrease in a cost due to the high-risk usage), while a low-risk usageprofile along the road segment may be associated with an adjustmentfactor less than one (indicating a lower cost due to low-risk usage). Insome embodiments, scores for a plurality of road segments along avehicle route may be used to determine a cost, estimate, or quote for ausage-based insurance charge, premium, or other cost, which may bepresented to a vehicle operator at the time of route selection to assistin selecting a route based upon safety, speed, cost, or otherconsiderations.

Once the one or more scores are calculated, they may be stored inprogram memory 160 or database 146 (block 514). At block 516, theexternal computing device 186 may then determine whether there remainany further road segments to be analyzed. If additional road segmentsare to be analyzed, the method 500 may continue by identifying anotherroad segment at block 508. If no additional road segments are to beanalyzed, the method 500 may continue to block 518. In some embodiments,block 518 may be excluded, in which case the method 500 may terminatewhen no additional road segments are to be analyzed.

At block 518, the external computing device 186 (such as a server 140)may generate an electronic map in some embodiments. The electronic mapmay comprise a plurality of map tiles including indications of thescores of road segments. In some embodiments, the map tiles may beoverlay to be superimposed upon other map tiles to indicate scores ofroad segments. In further embodiments, the electronic map may includemap tiles indicating only road segments for which one or more autonomousoperation features (e.g., a set of particular autonomous operationfeatures, particular types of autonomous operation features, orparticular levels of autonomous operation features) may be safely used(i.e., road segments meeting a minimum score threshold for safe use ofthe relevant autonomous operation features). In embodiments in which maptiles or overlay map tiles are generated, such tiles may be generatedeither as needed or in advance, but it is preferable to generate suchtiles in advance because of the processing time and resources requiredto generate such tiles. In other embodiments, the electronic map maycomprise an autonomous operation suitability map database of one or morescores (preferably a plurality of scores) for each road segment. Suchdatabase may be accessed to determine autonomous or semi-autonomousroutes for vehicles, as discussed elsewhere herein. In some embodiments,the electronic map (or portions thereof) may be communicated to a userdevice (e.g., the mobile device 110) to be displayed to a user.

Exemplary Autonomous Vehicle Routing Methods

FIG. 6 illustrates a flow diagram of an exemplary autonomous vehiclerouting method 600 for determining a route between predeterminedlocations to provide autonomous vehicle navigation support. The method600 may be used to identify and avoid locations where it may bedifficult or dangerous for the vehicle 108 to use autonomous operationfeatures. For example, autonomous operation may be unpredictable orhazardous when the vehicle encounters unexpected or temporary trafficpatterns, such as temporary lane shifts during construction. In certaincircumstances, it may be desirable to determine routes that avoid orminimize travel along road segments that are unsuitable for autonomousoperation feature use. The method 600 may be implemented to determineroutes that allow safe and fully autonomous travel (or any desired levelof autonomous feature use).

The method 600 may begin by receiving a first geospatial location (block602) and a destination geospatial location (block 604). Minimumrequirements for the route may be determined (block 606.) Relevant mapdata associated with autonomous operation scores of road segments maythen be accessed (block 608), such as from an autonomous operationsuitability map database, and the map data may then be used to identifyroad segments within the relevant map data meeting the minimumrequirements (block 610). The identified road segments may be examinedto determine whether at least one path between the first geospatialposition and the destination geospatial position exists (block 612). Ifno such path exists that meets the minimum requirements, one or moreparameters of the routing method may be adjusted (block 614) until suchpath exists. When one or more paths are determined to exist, an optimalroute between the first geospatial position and the destinationgeospatial position may be determined (block 616). An indication of theoptimal route may then be provided to a mapping or navigation system foruse in controlling the vehicle (block 618). The method 600 may beperformed by a server 140, by a mobile device 110 and/or on-boardcomputer 114, or by a combination of such components communicating vianetwork 130. Although the description below is presented using a mobiledevice 110 and server 140 for simplicity, the description below may beeasily modified for implementation by other systems including one ormore of a mobile device 110, on-board computer 114, or server 140.

At block 602, the mobile device 140 may receive a first geospatialposition. The mobile device 140 may further receive a destinationgeospatial position at block 604. The geospatial positions may bereceived as GPS or similar coordinates, street addresses, intersections,or any other indication of a specific location. In some embodiments, thefirst geospatial position may be received from a GPS unit 206 of themobile device 110. Such GPS data may indicate the current location ofthe vehicle 108 or a location of a user, such as a location from whichthe user wishes to depart. Alternatively, the user may select the firstgeospatial position by indicating a starting location of the route, suchas by entering an indication of the first geospatial position into themobile device 110. The user may similarly select the destination geospatial location directly or indirectly. As an example of indirectselection, the user may indicate that travel to a type of location(e.g., a gas station, a hospital, etc.) is desired, from which themobile device 110 may determine the destination geospatial location viacommunication with a map service via network 130. In some embodiments,both the first geospatial location and the destination geospatiallocation may be determined automatically in response to detectedconditions, as described further below. In further embodiments, eitheror both of the first and destination geospatial locations may beidentified or selected from a plurality of received common locations orroutes for a fleet of vehicles, such as frequent origin or destinationlocations for a fleet of personal transportation, commercial delivery,or other vehicles 108.

At block 606, the minimum requirements for the route may be determinedby the mobile device 110. The minimum requirements may relate to theacceptable range of scores for road segments along the route, such asrequiring a minimum score for each road segment. Such minimumrequirements may be selected by the user or may be automaticallydetermined based upon conditions of vehicle operation. For example, theuser may request a route suitable for fully autonomous operation. Asanother example, automatic emergency operation may require fullyautonomous operation throughout the route. In some embodiments, the usermay specify different minimum requirements for different types of roadsegments. For example, the user may require fully autonomous operationon highway road segments, but may allow semi-autonomous operation onresidential street road segments. In further embodiments, a user profilemay be created to indicate general user preferences regarding minimumroute requirements, which may vary by time, location, weather, otherenvironmental conditions, or whether the vehicle is operating in anemergency mode. For example, the user profile may indicate that a userprefers fully autonomous operation during weekday rush-hour operation.As another example, a user profile associated with a new driver mayrequire fully autonomous operation after a certain time or in inclementweather.

At block 608, the mobile device 110 may communicate the geospatiallocations and minimum requirements to the server 140 via the network130, causing the server 140 to access relevant map data from one or moredatabases 146. In some embodiments, the mobile device 110 maycommunicate additional information to the server 140 to facilitatedetermination of an optimal route. Such additional information mayinclude details regarding available types, configurations, settings, andoperating status of autonomous operation features (which may includeinformation regarding sensors 120 or software versions). Part or all ofthe additional information may be stored in a vehicle profile within thedatabase 146 to reduce data transmission over the network. The relevantmap data may be limited to road segments in a predefined oralgorithmically determined distance from the geospatial locations. Forexample, the map data may be accessed for the smallest map tile in themap database that includes both the first and destination geospatialpositions. Because the conditions of the operating environment (e.g.,time of day, traffic levels, weather, construction, etc.) impact theeffectiveness of the autonomous operation features, the server 140 maydetermine the condition of the relevant operating environment and accessthe map data associated with operation within the relevant operatingenvironment. For example, map data relating to autonomous orsemi-autonomous operation of vehicles on road segments at night may beaccessed if the route is to be traveled at night, while correspondingroad segment data associated with daytime travel may be ignored asirrelevant.

At block 610, the server 140 may identify the road segments meeting theminimum requirements for types and/or levels of autonomous operationfeature use from the accessed map data. This may include selecting roadsegments from the accessed map data that match multiple facets of theminimum requirements, such as meeting the separate minimum requirementsfor the operation of a plurality of autonomous operation features. Thus,the set of road segments identified as meeting the minimum requirementsmay be the intersection of the sets of road segments that meet eachfacet of the minimum requirements. In some embodiments, considerationsof legal proscriptions regarding use of autonomous operation features onroad segments may be used to determine whether such road segments meetthe minimum requirements. For example, some road segments may generallymeet the minimum requirements but may ban or require certain autonomousoperation feature use during certain periods (e.g., weekday rush hourperiods).

At block 612, the server 140 may determine whether at least one routeexists that forms a connected path between the first geospatial locationand the destination geospatial location along the identified roadsegments that meet the minimum requirements. This may includeiteratively checking road segments until either a connecting path isfound or all road segments have been checked. In some embodiments, thismay include a preliminary step of determining whether both the first anddestination geospatial positions lie along road segments that meet theminimum requirements, which may be used to quickly determine that nosuitable route exists if one or both geospatial locations are not upon aroad segment meeting the minimum requirements. If at least one pathbetween the first and destination geospatial locations is found, themethod 600 may continue with determining an optimal route (block 616).If no paths meeting the minimum requirements are found, the method 600may instead attempt to adjust the parameters (block 614) to find asuitable route. Once the parameters have been adjusted (block 614), themethod 600 may continue by accessing map data using the new parameters(block 608). Alternatively, the method 600 may notify the user that nosuitable route exists or may terminate with an error message if nosuitable path is found.

At block 614, the server 140 may adjust one or more parameters in anattempt to find a route suitable for the requested type of autonomous orsemi-autonomous operation. This may include adjusting the minimumrequirements to include road segments that are near the original minimumrequirements (e.g., within 5% of the original minimum score threshold).If a legal proscription against certain types or levels of autonomousoperation along particular road segments exists, however, such roadsegments may be separately treated as unavailable for adjustment. Insome embodiments, the adjusted parameters may be parameters other thanthe minimum requirements. Such other parameters may include distancefrom the first and destination geospatial locations, use of toll roads,or similar parameters involving the scope of the accessed the map data.For example, additional map data tiles may be included, such asoverlapping or larger map data tiles. This may correspond to drivinggenerally away from the destination geospatial location before travelingtowards it. Although such routes may be longer, the additional roadsegments may facilitate travel in a manner that meets the minimumrequirements everywhere along the route.

In further embodiments, adjusting the parameters may include allowingfor the inclusion of short distances of road segments that may besuitable for significantly less autonomous or semi-autonomous operation.For example, road segments of less than one mile that connect on bothends to road segments meeting the minimum requirements (or that connectto or contain the first or destination geospatial locations) may beincluded, even if such road segments are not suitable for any autonomousoperation feature use (or are suitable for only the lowest levels ofsuch feature use). This may allow the driver to travel most of the tripautonomously using an efficient route, but the route may require thedriver to take control for a short distance (e.g., while passing througha construction zone).

Similarly, in instances in which a suitable route cannot be foundbecause the first geospatial location or the destination geospatiallocation are not located along a road segment that meets the minimumrequirements, a substitute geospatial location along a road segment thatmeets the minimum requirements may be determined. Such substitutegeospatial position may be used to determine routes between a substitutefirst geospatial position and the destination geospatial position,between the first geospatial position and a substitute destinationgeospatial position, or between a substitute first geospatial positionand a substitute destination geospatial position. For example, a pick-upor drop-off location requested by the user may be adjusted to facilitateautonomous or semi-autonomous operation along a route of road segmentsmeeting the minimum requirements.

Once at least one route is found that forms a connected path between thefirst geospatial location and the destination geospatial location alongthe identified road segments that meet the minimum requirements, theserver 140 may determine one or more optimal routes between thegeospatial positions at block 616. Where substitute geospatial positionshave been determined, of course, the route will use such substitutegeospatial positions as origin or terminal points. Routes may beoptimized relative to metrics such as time, distance, total risk,continuity of progress (i.e., avoiding stops), amount of fullyautonomous operation, amount of manual operation, amount of operation ator above the minimum requirements, fuel use, and/or other metrics. Forexample, the optimized route may maximize a distance or an amount oftime that the autonomous vehicle travels in autonomous mode, or theoptimized route may minimize a distance or time that the autonomousvehicle travels in manual mode. In some instances, a unique optimalroute may be determined, while other instances may identify multipleoptimal routes that are equivalent (or within statistical margins oferror) for the relevant one or more metrics. The optimal route may bethe safest route, the route associated with a least amount of pedestriantraffic or cross walks, the quickest route, the shortest route, or theroute with most highway driving.

The optimal route may include the highest percentage of autonomousfeature usage or autonomous mode operation, or may include 95% to 100%autonomous mode operation along the route. The optimal route may be theshortest route (in time or mileage) that includes the highest percentageof autonomous feature usage or autonomous mode operation. The optimalroute may be the shortest route (in time or mileage) that includes apercentage of autonomous feature usage or autonomous mode operation overa predetermined threshold, such as 50%, 60%, 75%, 80%, or 90%. Theoptimal route may be the shortest route (in time or mileage) thatincludes 100% autonomous feature usage or autonomous mode operation overthe route. The optimal route may similarly be a route associated withthe lowest risk, or the fastest or shortest route below a maximumtolerable risk threshold. The risk may be determined based upon a riskprofile for the vehicle 108 and/or a user profile for the vehicleoperator.

Some embodiments may include determining a plurality of optimal routes,each of which optimizes some set of one or more metrics (e.g., thefastest route, the shortest route, or the cheapest route based upontotal costs of operation including fuel, wear, insurance, tolls, etc.).In embodiments in which a route may include one or more road segmentswhere manual operation or semi-autonomous operation is required, theoptimal routes may further be determined based at least in part upon theamount of manual or semi-autonomous operation required, or the level ofmanual or semi-autonomous operation required. In further embodiments,one optimal route may be selected from alternative optimal routes,either by application of automated decision criteria or by receiving auser selection.

At block 618, the server 140 may then provide the determined optimalroute (or routes) to the mobile device 110 for use in vehiclenavigation. The mobile device 110 may present the optimal route (orroutes) to the user for review and approval in some embodiments. Forexample, one or more optimal routes determined above may be presented tothe user via a display 202 associated with the mobile device 110 oron-board computer 114 as recommendations. Such recommendations mayinclude additional information regarding risks, time, or costsassociated therewith. For example, costs associated with adjustments toinsurance policy premiums, discounts, or other terms may be presented tothe user with one or more recommendations. In further embodiments, theoptimal route may be communicated to the on-board computer 114 of thevehicle 108 to cause the vehicle 108 to operate autonomously along theoptimal route, such as in emergency situations or when a fullyautonomous trip is requested. In still further embodiments, presentingthe optimal route or routes may include generating notifications ofwhere (and when) autonomous mode or manual mode is required orrecommended along individual routes or roads, such as notifications of(1) when or where the driver should manually operate/drive theautonomous vehicle, (2) when or where the autonomous system should driveor control the autonomous vehicle, and/or (3) when or where certainautonomous features or system should be engaged or utilized, and atwhich setting or configuration individual autonomous systems or featuresshould be engaged. In some embodiments, the optimal route may be furtherused to determine a cost, estimate, or quote for a usage-based insurancecharge, premium, or other cost, which may be presented to a vehicleoperator at the time of route selection to assist in selecting a routebased upon safety, speed, cost, or other considerations. Vehicle use mayfurther be monitored to determine whether the recommended optimal routeis followed, which may be used to adjust risk levels and/or costsassociated with insurance accordingly.

Automatic Feature Usage Optimization

Information regarding the suitability of road segments for autonomous orsemi-autonomous vehicle operation may also be used to maintain properfeature usage automatically during operation. As a vehicle traversesvarious road segments and as conditions change, the optimal usage levelsof autonomous operation features (i.e., the settings and configurationsthat are most efficient, safest, etc.) may likewise change. For example,some roads segments may be designated as unsuitable for higher levels ofautonomous operation, while other roads or portions thereof may requirea minimum level of autonomous operation (such as major commuter arteriesduring rush hour periods). Without road-segment specific informationregarding suitability (as discussed above), the autonomous operationfeatures simply continue to operate using the most recently specifiedsettings until adjusted by a vehicle operator. Adjusting the settingsassociated with autonomous operation features (including enabling ordisabling features) is time-consuming, and doing so may also behazardous when the vehicle operator is in partial control of a vehicleoperating in a semi-autonomous mode. Even when the vehicle operator hassufficient information and is able to safely adjust the usage levelsduring operation, such adjustments made through a user interface resultin a delay from user control. Thus, the automatic methods of adjustingor configuring autonomous operation feature usage levels describedherein improve upon existing techniques.

FIG. 7 illustrates a flow diagram of an exemplary automatic usageoptimization method 700 for monitoring and adjusting autonomousoperation feature usage levels of a vehicle 108 having a plurality ofautonomous operation features. The method 700 may begin by receivingautonomous operation suitability data for a plurality of road segments(block 702), which may form a route the vehicle 108 will traverse. Whilevehicle operation continues, vehicle operation may be monitored for eachroad segment along which the vehicle 108 travels (block 704), which mayinclude obtaining vehicle operating data relating to location andenvironmental conditions. Based upon the monitored vehicle operatingdata and the suitability data, appropriate usage levels for one or moreautonomous operation features may be determined (block 706), which mayinclude only optimal settings or may include other allowable settings.The determined appropriate usage levels may be compared against currentusage levels to determine whether adjustments are needed (block 708).When the current usage levels are not appropriate for the road segmentand conditions, the usage levels of the one or more autonomous operationfeatures may be automatically adjusted to appropriate levels (block710). Such adjustments may include enabling or disabling features, aswell as adjusting settings associated with the features. Once the usagelevels are at appropriate levels, the method may check whether thevehicle 108 has reached the end of its route or whether operation isongoing (block 712). The method 700 may then continue to monitor vehicleoperation and adjust usage levels as needed (blocks 704-710) until thevehicle operation is determined to be discontinued (block 712), at whichpoint the method may terminate. Although the method 700 is describedherein as being implemented by an on-board computer 114 of the vehicle108, other components may implement part or all of the method, such as amobile device 110 within the vehicle 108 and/or a server 140communicating with the vehicle 108 via a network 130.

At block 702, the on-board computer 114 may receive suitability datarelating to autonomous operation features usage for a plurality of roadsegments. The suitability data may include risk levels or suitabilityscores indicating the suitability or permissibility of autonomousoperation feature usage for the road segments. The suitability data maybe included in map data received from a map database or map server viathe network 130, either directly or through a mobile device 110. Suchmap data may further include location data associated with each of theplurality of road segments (such as GPS data), which may be used toidentify current road segments where the vehicle 108 is currentlylocated during operation. The map data may be received with or withoutgraphical map tile data, in various embodiments. The suitability datamay be received for the plurality of road segments based upon a route ofthe vehicle 108, such as a route determined by the methods describedelsewhere herein. Alternatively, suitability data may be received for aplurality of road segments in proximity to the current location of thevehicle 108 (e.g., all road segments within a predetermined distance ofthe current location). The suitability data may be requested by theon-board computer 114 or may be received from a database without arequest. In some embodiments, the on-board computer 114 may store thesuitability data in the program memory 208 for use throughout a vehicletrip or multiple vehicle trips. For example, the on-board computer 114may store suitability data for a plurality of road segments in an areaof frequent operation of the vehicle 108 for repeated use over a periodof time, which stored suitability data may be updated periodically bycommunication with a server 140. The on-board computer 114 may thenaccess the map data as needed.

Although the exemplary method 700 describes an embodiment in which thesuitability data is obtained for a plurality of road segments in advanceof use, alternative embodiments may obtain the suitability data for eachroad segment as the vehicle operates. Thus, the suitability data mayonly be received for current road segments (or immediately upcoming roadsegments) in some embodiments. One of ordinary skill in the art willunderstand that such modification to the method 700 may be made bysimply obtaining the suitability data for each road segment separatelybased upon an indication of the location of the vehicle 108, such as byaccessing a remote database of map data to obtain the suitability databased upon current vehicle locations.

At block 704, the on-board computer 114 may monitor operation of thevehicle 108 during operation. Such monitoring may include collectingoperating data for the vehicle 108, or it may be limited to monitoringlocation, current autonomous operation feature usage levels, and one ormore environmental conditions in the vehicle operating environment. Thelocation of the vehicle 108 may be determined using the GPS unit 206 orother geolocation components within the vehicle (including geolocationcomponents within the mobile device 108). An indication of such locationmay be used to determine the current road segment the vehicle istraversing along a route. For example, the vehicle 108 may be proceedingalong a route between origin and destination locations, which route mayinclude a plurality of connecting road segments. The operation of thevehicle 108 may be monitored in part using the sensors 120 within thevehicle 108 to determine location and environmental conditions, such asweather, traffic, construction, or other conditions. In someembodiments, the sensors 120 may also monitor a vehicle operator todetermine condition of the vehicle operator (e.g., attentiveness,distraction, drowsiness, impairment, etc.), which may be used todetermine the relative risks of manual and autonomous operation. Infurther embodiments, the on-board computer 114 may further determine astatus of the one or more autonomous operation features, which mayinclude an evaluation of whether the autonomous operation features arefunctioning properly, damaged, or inoperable. Similar status or qualitydeterminations may be made for sensors 120 used by the autonomousoperation features, the status of which may impact the effectiveness ofthe features in controlling the vehicle 108.

At block 706, the on-board computer 114 may determine appropriate usagelevels for the one or more autonomous operation features of the vehicle108 for the current road segment under current operating conditions. Theon-board computer 114 may identify the current road segment from theplurality of road segments based upon the determined current location ofthe vehicle 108 and the location data associated with each of theplurality of road segments for which suitability data was received. Thismay include accessing stored map data and selecting the road segmentassociated with GPS coordinates matching the current GPS coordinates ofthe vehicle 108. In some embodiments, an upcoming road segment mayadditionally or alternatively be determined based upon the currentlocation of the vehicle 108 and information regarding a route of thevehicle 108. The information regarding the route may include a fullroute for a vehicle trip or information regarding a likelihood of thevehicle 108 following a particular route, such as a prediction basedupon prior vehicle operation or based upon an assumption that thevehicle 108 will continue to follow the road along an additional roadsegment. For example, a section of limited access highway may covermultiple road segments having differing risk or suitability forautonomous operation, such as a bridge that may present difficulties forsome autonomous operation features. Analysis of upcoming road segmentsmay be beneficial in order to provide advance warning to the vehicleoperator if manual operation is required.

The current operating conditions may include environmental conditionsand other monitored conditions associated with the vehicle 108 (e.g.,autonomous operation feature status, vehicle operator condition, etc.).Such environmental conditions may include weather, traffic,construction, time of day, day of week, season, or other environmentalfactors that may affect risk levels associated with operation of thevehicle 108 on the road segment. The current operating conditions maysimilarly include other non-environmental conditions, such as conditionsrelating to the vehicle operator's current state or capability formanually operating the vehicle or the status of one or more componentsof the vehicle that may affect the autonomous operation features'quality or reliability in controlling the vehicle 108.

Determining the appropriate usage levels for the autonomous operationfeatures of the vehicle 108 may include determining one or more usagelevels that are optimal in terms of risk, speed, user preferences, orother metrics, which optimal usage levels may be determined based uponthe suitability data for the road segment and the current operatingconditions. Such optimal usage levels may be determined by comparison ofmetrics determined for the current usage levels and each of a pluralityof alternative usage levels to identify one or more that maximize orminimize a relevant metric or a weighted combination of metrics. Inparticularly preferred embodiments, determining the appropriate usagelevels may include determining one or more settings associated with theone or more autonomous operation features that minimize risk based uponthe road segment suitability data and the current operating conditions.The appropriate usage levels may be determined using constrainedoptimization techniques, where allowed usage levels for the road segmentor minimum levels of autonomous operation based upon vehicle operatorcondition may constrain the range of usage level within which operationmay be optimized.

In some embodiments, more than one usage level may optimize operatingcriteria, such as where the risk levels associated with differentcombinations of settings are identical, such as where the risk levelsassociated with the different combinations of settings are below athreshold generally associated with safe operation, or where thedifference in risk levels are within a predetermined threshold. When aplurality of combinations of settings are associated with optimal usagelevels, a secondary metric may be used to determine in part one of thecombinations as the optimal usage level. Such secondary metrics mayinclude a measure of statistical variance in risk levels associated withthe combinations. Alternatively, a plurality of optimal usage levels mayall be identified as appropriate usage levels and used to determinewhether adjustments to the current usage levels are required.

The appropriate usage levels may alternatively be determined as one ormore usage levels that meet operational requirements for the roadsegment of interest (i.e., the current road segment or upcoming roadsegment). Such operational requirements may include legal restrictionsagainst or requirements for certain levels of autonomous operation ofvehicles along the road segment, which may be based upon governmentalregulations or contractual obligations (e.g., insurance policy covenantsor warranty limitations). Such operational requirements may similarlyinclude minimum safety levels, minimum or maximum autonomous operationlevels, or other metrics specified in a user profile.

At block 708, the on-board computer 114 may next determine whether thecurrent usage levels are consistent with the determined appropriateusage levels. This determination may include determining whether thecurrent settings of the one or more autonomous operation features matchthe settings of an optimal usage level or other appropriate usagelevels. Where multiple appropriate usage levels have been determined,the appropriate usage level requiring the fewest changes in settings orthe least noticeable changes in settings may be selected. Thus, if thecurrent settings match the settings associated with an appropriate usagelevel, the on-board computer 114 may determine that the current usagelevels are appropriate. Otherwise, the on-board computer 114 maydetermine one or more usage level changes corresponding with changes tosettings of at least one of the one or more autonomous operationfeatures of the vehicle 108 by comparing the current settings againstthe settings associated with an appropriate usage level. When one ormore usage level changes have been determined to be necessary, themethod may proceed to implement such changes at block 710.

At block 710, the on-board computer 114 may adjust the settings of theat least one autonomous operation feature to implement the one or moredetermined usage level changes. The settings may be automaticallyadjusted by the on-board computer 114 to match the selected optimal orotherwise appropriate usage levels without notification to the vehicleoperator in some embodiments. When the adjustment would result in manualoperation or an increase in manual operation by the vehicle operator,however, the on-board computer 114 may first determine whether thevehicle operator is capable and prepared to assume control. For example,a road segment may require manual operation of the vehicle (orsemi-autonomous operation primarily controlled by the vehicle operator).Thus, the on-board computer 114 may determine vehicle operatorpreparedness for vehicle operation based upon sensor data regarding thestate of the vehicle operator. The on-board computer 114 may furtherpresent a notification or alert to the vehicle operator prior toadjusting the settings to increase manual control. In some embodiments,the on-board computer 114 may wait until the vehicle operator hasacknowledged the alert to hand off control from an autonomous operationfeature to the vehicle operator. Because such changeover of controlbetween an autonomous operation feature and the vehicle operator mayrequire a time delay, the on-board computer 114 may determine the changefor an upcoming road segment and alert the vehicle operator in advanceof reaching the upcoming road segment in some embodiments.

At block 712, the on-board computer 114 may determine whether thevehicle 108 has reached the end of its current route. The end of theroute may be determined based upon information indicating an end-pointof a route or may be determined by the cessation of vehicle operation(e.g., when the vehicle is parked or shut down). Until the end of theroute is reached, the on-board computer 114 may continue to monitor theoperation of the vehicle 108 as described above. When the end of theroute is determined to be reached, the method 700 may terminate.

Automatic Feature Usage Restrictions

Vehicle operators may vary in their levels of experience operatingvehicles, impairment levels, or ability to effectively monitor andrespond to conditions while manually controlling vehicles. For example,new drivers may be capable of safely piloting a vehicle in good weatherin light traffic, but they may lack the ability to safely drive in morechallenging conditions. As another example, drivers with poor eyesightmay be significantly less safe when driving at night or in inclementweather. Further, vehicle operators or owners may wish to prevent manualoperation when by operators who are impaired (e.g., drowsy, distracted,or intoxicated). Existing systems and methods allow vehicles to begenerally limited, such as by using a speed limiter to prevent a vehiclefrom exceeding a maximum speed or testing a vehicle operator's breathfor the alcohol before allowing the vehicle to be started. Theseexisting techniques do not distinguish between multiple users of thesame vehicle, nor do they adjust to the changing conditions within whichthe vehicle operates. The methods described herein, however, can be usedto automatically adjust vehicle usage restrictions based upon bothvehicle operator identity and current conditions. Thus, the methodsdescribed herein improve upon the existing techniques by enabling moreuseful and targeted restrictions on manual vehicle operation than couldpreviously have been implemented.

FIG. 8 illustrates a flow diagram of an exemplary manual vehicle controlrestriction method 800 for limiting manual control of a vehicle 108capable of varying levels of manual and autonomous operation. The method800 may begin by identifying the vehicle operator (block 802) andaccessing a user profile for the vehicle operator (block 804). Duringvehicle operation, information regarding the vehicle environment andvehicle operation may be obtained (block 806) and evaluated to determineallowable settings for the one or more autonomous operation features ofthe vehicle 108 based upon the user profile (block 808). Currentsettings may be compared against the allowable settings to determinewhether the current settings are within the range of allowable settingsfor the vehicle operator under current operating conditions (block 810).When the current settings are not within the range of allowablesettings, the settings may be automatically adjusted to comply with therestrictions indicated by the user profile (block 812). Once thesettings match the allowable settings, the method may determine whetheroperation of the vehicle 108 is ongoing (block 814). The method 800 maythen continue to monitor vehicle operation and adjust settings as needed(blocks 806-812) until vehicle operation is determined to bediscontinued (block 814), at which point the method may terminate.Although the method 800 is described herein as being implemented by anon-board computer 114 of the vehicle 108, other components may implementpart or all of the method, such as a mobile device 110 within thevehicle 108 and/or a server 140 communicating with the vehicle 108 via anetwork 130.

At block 802, the on-board computer 114 may identify the vehicleoperator of the vehicle 108. The vehicle operator's identity may bedetermined based upon a personal electronic key or other personalelectronic device associated with the vehicle operator, such as a mobiledevice 110 (e.g., a smartphone or wearable device). The on-boardcomputer 114 may identify the vehicle operator by recognizing thepersonal electronic device when connected, such as via a wirelessconnection (e.g., a Bluetooth® connection). In some embodiments, theon-board computer 114 may receive sensor data regarding the vehicleoperator from one or more sensors 120 disposed within the vehicle 108,which sensor data may be used to identify the vehicle operator. Forexample, image data from a camera may be evaluated to identify thevehicle operator based upon facial features or the vocal profile of thevehicle operator may be used to identify the vehicle operator based uponsound data from a microphone. In other embodiments, the vehicle operatormay be identified by receiving a personal identifier indicated by thevehicle operator via an input device associated with the on-boardcomputer 114, which may be a number or user name entered or selected bythe vehicle operator.

At block 804, the on-board computer 114 may access a user profileassociated with the identified vehicle operator. The user profile may bestored in a program memory 208 of the on-board computer 114 or may beretrieved from a mobile device 110 or server 140. The user profile maystore information regarding the vehicle operating, including informationregarding general operating ability levels or risk levels. The userprofile may likewise store indications of one or more allowed usagelevels for the vehicle operator in a plurality of operating conditions(e.g., weather, traffic, time of day, construction, etc.). For example,the user profile for a new driver may indicate that manual operation isprohibited at certain times (e.g., late night and early morning) andduring icy conditions (e.g., when the temperature and recentprecipitation levels indicate an elevated risk of icy roads). As anotherexample, the user profile may prohibit any manual operation while thevehicle operator is using a mobile device 110, which may include usinghands-free telecommunication features to conduct a phone call.

In some embodiments, the user profile may include indications ofmultiple levels of semi-autonomous vehicle operation in which neitherthe vehicle operator nor the autonomous operation features completelycontrol vehicle operation. Such semi-autonomous operation levels may beparticularly useful for reducing risk while allowing new drivers to gainexperience. In further embodiments, the user profile may indicateautonomous operation usage level restrictions by risk levels, which mayindicate absolute risk levels or relative risk levels above the risklevels associated with autonomous operation. For example, the userprofile may indicate that the vehicle operator is prohibited from manualoperation of the vehicle 108 when the risk of an accident from suchoperation exceeds a full-autonomous threshold, or the vehicle operatormay be required to use certain levels or setting of the autonomousoperation features during semi-autonomous operation when risk levelsexceed corresponding semi-autonomous thresholds. The user profile mayalso store user preferences, such as preferences for automaticallyengaging autonomous operation features in certain conditions (e.g., whenmanual operating risk is high or when in traffic) for the convenience ofthe vehicle operator.

At block 806, the on-board computer 114 may obtain information regardingthe vehicle's operating environment and the operation of the vehicle108. The environmental information may be collected by the on-boardcomputer 114 from one or more sensors 120 of the vehicle 108, which maybe processed to determined one or more environmental conditions. Suchenvironmental conditions may include light levels, location, type ofroadway, speed limit, traffic levels, weather conditions, orconstruction. For example, sensor data from a GPS unit 206 may be usedto determine a location, which may further be used to determine a typeof roadway. As another example, sensor data from a camera may be used todetermine a posted speed limit. Other environmental conditions mayinclude information obtained from sources other than sensors, such as atime of day.

In addition to information regarding the vehicle operating environment,the on-board computer 114 may monitor vehicle operation by the vehicleoperator or the autonomous operation features. For example, the qualityof control decisions by the autonomous operation features may beevaluated by the on-board computer 114 to determine whether the featuresare functioning properly or to estimate risk levels based uponautonomous operation feature usage. Such monitoring of autonomousoperation feature usage may also include determining current usagelevels or settings associated with the one or more autonomous operationfeatures of the vehicle 108 in order to ensure compliance with therestrictions indicated by the user profile. In some embodiments,information regarding operation of the vehicle 108 by vehicle operatormay be collected for use in updating the user profile, such as byadjusting estimated risk levels associated with manual operation by thevehicle operator under certain environmental conditions. Suchinformation regarding vehicle operation may include sensor data obtainedfrom the sensors 120 of the vehicle 108, such as acceleration data,image data relating to gaze location or mirror checking, or othersimilar sensor data regarding the movement of the vehicle 108 or theactions of the vehicle operator.

At block 808, the on-board computer 114 may determine one or moreallowable usage levels or settings for the autonomous operation featuresof the vehicle 108 based upon the user profile. This determination mayinclude determining one or more environmental conditions from theenvironmental data, then determining the allowable usage levels from theuser profile based upon the determined environmental conditions.Similarly, the determination may include determining one or more risklevels based upon the environmental conditions or the operating dataregarding operation of the vehicle 108, which may be further evaluatedto determine one or more risk levels associated with operation of thevehicle 108 under the current conditions. Such risk levels may be usedto determine allowable usage levels based upon the indications in theuser profile.

In some embodiments, the allowable usage levels may be determined inpart by the suitability of the road segment on which the vehicle 108 iscurrently operating, which may be determined as described elsewhereherein. The on-board computer 114 may identify a current road segmentbased upon a location of the vehicle 108, obtain suitability informationfor such road segment, and determine the allowable usage levels eitherdirectly or indirectly using the suitability data. For example, thesuitability data may constrain the determination of the allowable usagelevels by requiring or prohibiting some usage levels. Alternatively,risk levels for the road segment under relevant environmental conditionsmay be used to determine usage levels that meet risk-based requirementsindicated by the user profile. In some embodiments, the allowable usagelevels or settings may be determined as one or more ranges of settingsor allowable combinations of usage levels or settings for the autonomousoperation features.

At block 810, the on-board computer 114 may determine whether thecurrent usage levels or settings associated with the autonomousoperation features of the vehicle 108 comply with the determinedallowable usage levels or settings. This determination may includedetermining whether the current settings or other current usageindicators of each of the autonomous operation features match the samecombination of allowable usage levels or settings. The determination maysimilarly include determining whether the current settings or othercurrent usage indicators of each of the autonomous operation featuresare within one or more ranges associated with the allowable usage levelsor settings. When the current usage levels or settings are determined tobe compliant with the determined allowable usage levels or settings, theon-board computer 114 may determine whether vehicle operation iscontinuing at block 814. When the current usage levels or settings aredetermined not to be compliant with the determined allowable usagelevels or settings, the on-board computer 114 may proceed to adjust theusage levels or settings to comply with the allowable usage levels orsettings at block 812 before determining whether vehicle operation iscontinuing at block 814.

At block 812, the on-board computer 114 may adjust at least oneautonomous operation feature to comply with the allowable usage levelsor settings. To adjust the autonomous operation features, the on-boardcomputer 114 may determine a usage level change associated with thefeatures to be adjusted, indicating changes to usage levels or settingsassociated with the autonomous operation features to be adjusted basedupon the allowable usage levels or settings. In some embodiments, acombination of usage levels or settings associated with the autonomousoperation features may be selected from the allowable usage levels orsettings that requires the fewest changes or least disruptive changes tocurrent usage levels or settings. Alternatively, the usage level changemay be determined as one or more changes to settings associated with theautonomous operation features based at least in part upon risk levels,such as by minimizing risk levels or determining changes to bring risklevels within allowable levels indicated by the user profile.

In determining risk levels, the on-board computer 114 may includeinformation regarding the environmental and other operating conditionsin which the vehicle 108 is currently operated. Once the usage levelchange has been determined, the on-board computer 114 may adjust one ormore settings of one or more autonomous operation features based uponthe determined usage level change to comply with the allowable usagelevels or settings.

At block 814, the on-board computer 114 may determine whether vehicleoperation is ongoing. Operation may be determined to be ongoing untilthe cessation of vehicle operation is detected (e.g., when the vehicleis parked or shut down) or until the vehicle 108 has reached the end ofits current route. The end of the route may be determined based uponinformation indicating an end-point of a route. Until the end of theroute is reached, the on-board computer 114 may continue to monitor theoperation of the vehicle 108 to ensure compliance with the restrictionsor limitations on autonomous operation feature usage indicated by theuser profile, as described above at blocks 806-812. Thus, the method 800may determine whether usage levels comply with the allowable usagelevels at each of a plurality of road segments along a route andimplement adjustments to the usage levels as need to maintain compliancewith the allowable usage levels or settings.

Following completion of vehicle operation, in some embodiments, theon-board computer 114 may update the user profile based upon informationobtained during vehicle operation at block 816. The on-board computer114 may evaluate operating data collected and stored during vehicleoperation to determine risk levels, skill levels, or competency levelsassociated with aspects of manual vehicle operation by the vehicleoperator. For example, observations of good driving habits based uponsensor data (e.g., maintaining safe distances from other vehicles ormaintaining good lane centering) may be used by the on-board computer114 to reduce estimated risk levels associated with manual vehicleoperation in the user profile. In some embodiments, adjustments to theuser profile may be communicated to a server 140 via a network 130 toallow the server 140 to update the user profile. In further embodiments,operating data (or a summary thereof) may be received by the server 140and used by the server 140 to update the user profile risk levels. Insome embodiments, adjustments to indications of allowable usage levelsor settings may be made in the updated user profile.

Such changes may be determined based upon experience metrics or risklevel thresholds. For example, when risk levels associated with manualoperation in icy conditions are sufficiently low, the user profile maybe updated to allow partial or fully manual operation in icy conditions.In further embodiments, the user profile may be updated during vehicleoperation. The updated user profile may then be used in determiningallowable usage levels or settings of autonomous operation featureduring future vehicle use by the vehicle operator.

Automatic Vehicle Refueling and Recharging

With information regarding vehicle usage, it is possible to predict andschedule vehicle refueling or recharging without vehicle operatorinvolvement. Such refueling or recharging may be performed when thevehicle is parked or during a vehicle trip (e.g., while passengers areeating lunch). Although refueling of traditional internal combustionvehicles is typically performed quickly, electric vehicles typicallyrequire a lengthy period to recharge their batteries. Therefore,scheduling of recharging is particularly advantageous of autonomouselectric vehicles. Currently, vehicle operators must manually determinewhen to refuel or recharge vehicles. To perform such manual refueling orrecharging, vehicle operators observe fuel or charge gauges on a vehicledashboard and estimate when to refuel based upon knowledge or guessesabout availability of fueling or charging stations. In the case ofelectronic vehicles, the vehicle operator must also plan for a lengthyperiod when the electronic vehicle cannot be used during charging.Because of this, charging has been a significant problem that hashindered the adoption of electronic vehicles. The methods describedherein solve the problems associated with refueling or rechargingautonomous vehicles by automating the process to be performed at anopportune time and without vehicle operator involvement.

FIG. 9 illustrates a flow diagram of an exemplary automatic refueling orrecharging method 900 for autonomous vehicles capable of fullyautonomous operation. The method 900 may begin, in some embodiments, bydetecting that an autonomous vehicle 108 is not currently in use (block902). In other embodiments, the method 900 may instead begin bydetecting a fuel level or battery charge level (block 904), which mayinvolve estimating remaining fuel or charge based upon information fromsensors 120 within the vehicle 108. Future use of the vehicle 108 may bepredicted (block 906), which may include determining a predicted useprofile indicating one or more expected future uses of the vehicle 108.The fuel or battery charge level may be used to determine whetherrefueling is appropriate based upon a maximum threshold (block 908).When the fuel or battery level charge level is below the maximumthreshold, the time available for refueling or recharging may beestimated based upon the predicted future use and evaluated to determinewhether sufficient time is available for refueling or recharging (block910). Even if sufficient time is not available to refuel or recharge thevehicle 108 without interfering with expected vehicle usage, refuelingor recharging may nonetheless be performed if the fuel or battery chargelevels are determined to be below a minimum threshold (block 912). Thevehicle owner or operator may set such maximum and minimum thresholds toavoid completely depleting the fuel tank or battery during operation.

Once refueling or recharging is determined to be appropriate, a time andlocation for refueling or recharging may be determined (block 914), anda fully autonomous route to the location may be obtained (block 916). Atthe appropriate time (which may be immediately following receipt of theroute information), the vehicle 108 may be controlled fully autonomouslyalong the route to the refueling or recharging location (block 918) andcause the vehicle 108 to refuel or recharge (block 920). Upon completionof refueling or recharging, a fully autonomous return route to a returnlocation may be obtained (block 922), and the vehicle 108 may becontrolled fully autonomously along the return route to the returnlocation (block 924). The vehicle 108 may then park at the returnlocation or proceed to a further destination, as directed by a vehicleoperator. Although the method 900 is described herein as beingimplemented by an on-board computer 114 of the vehicle 108, othercomponents may implement part or all of the method, such as a mobiledevice 110 within the vehicle 108 and/or a server 140 communicating withthe vehicle 108 via a network 130.

At block 902, in some embodiments, the on-board computer 114 may detectthat the vehicle 108 is not currently in use. In such embodiments, theon-board computer 114 may detect non-use and perform the method 900whenever the vehicle 108 is parked, shut down, or dropped off forparking. For example, the vehicle 108 may be considered to be not in useafter dropping off all passengers and being directed to proceedautonomously to a parking location. In other embodiments, the on-boardcomputer 114 may only perform the method 900 when the vehicle 108 isdetermined not to be in use at a location associated withlonger-duration parking, such as a garage. Embodiments including thedetermination of the vehicle not being in use may be advantageous inminimizing interference with vehicle use. In alternative embodiments,the method 900 may be performed while the vehicle 108 is in use, such asduring an extended vehicle trip.

At block 904, the on-board computer 114 may detect a fuel level orbattery charge level of the vehicle 108. A fuel level may be determinedfor fuel-burning autonomous vehicles, while a battery charge level maybe determined for autonomous electric vehicles. This may includedetecting a fluid level within a fuel tank or charge informationassociated with a battery (e.g., voltage or current), which may then beused to determine a fuel level or a battery charge level indicatingremaining fuel or charge. In some embodiments in which the vehicle 108is not in use, the last measurements of fluid level or chargeinformation during vehicle operation may be used without detecting suchmetrics again while the vehicle is not in use. In embodiments in whichthe vehicle 108 is in use, the on-board computer 114 may detect thefluid level or charge information and determine the fluid level orbattery charge level during operation. In further embodiments, theon-board computer 114 may determine an estimate of maximum remaining usebefore refueling or recharging in distance or time for the vehicle 108based upon the fuel level or battery charge level, which may bedetermined based upon average observed efficiency levels during vehicleoperation. Such maximum remaining use estimate may be useful indetermining whether refueling or recharging is desirable or necessary.

At block 906, the on-board computer 114 may predict future use of thevehicle 108. Such prediction may be based upon data regarding pastvehicle operation, location data, vehicle operator calendar data, routedata, or other information related to the vehicle 108 or vehicleoperators associated with the vehicle 108. For example, a use profilemay be generated during vehicle use to indicate patterns of repeatedvehicle use, such as usual commuting routes and hours. Similarly,location data (such as long-term parking at an airport) or calendar datafor a vehicle operator (such as a schedule of meetings) may indicate thevehicle will likely not be used for a period of time. In someembodiments, the on-board computer 114 may determine a predicted useprofile for the vehicle 108, which may be based upon past use or otherdata associated with the vehicle 108 or the vehicle operator. Thepredicted use profile may include one or more indications of expectedfuture uses of the vehicle 108, such as a prediction of the next use ora plurality of expected use periods (i.e., times associated withpredicted use of the vehicle 108). The predicted use profile maylikewise include probabilities of vehicle use associated with aplurality of time periods (e.g., hours). In some embodiments, thepredicted use profile may include predictions of periods of use andnon-use over a range of one or more days.

Where the vehicle 108 is currently in use, the predicted use profile mayinclude predictions of use periods and/or non-use period, such as breaksduring vehicle operation. Route data may be used to predict such breaks,as the route may indicate the purpose or extend of a current vehicletrip. For example, a long vehicle trip of several hundred miles may beindicated by the route data, from which the on-board computer 114 maypredict likely breaks based upon the duration of the trip or informationregarding past vehicle trips (e.g., a likely break for lunch). Infurther embodiments, the predicted use profile may be determined by aserver 140 or mobile device 110 upon receipt of a request by theon-board computer 114. The predicted use profile may be communicatedback to the on-board computer 114, or the server 140 or mobile device110 may perform further analysis using an indication of the fuel levelor battery charge level from the on-board computer 114.

At block 908, the on-board computer 114 may determine whether the fuellevel or battery charge level is below a maximum threshold for refuelingor recharging. Such maximum threshold may be predetermined as apercentage of maximum capacity, a distance of vehicle operation, or atime duration of vehicle operation. Alternatively, the maximum thresholdmay be determined by the on-board computer 114 based upon data regardinga vehicle route. For example, the maximum threshold may be determined asa required level of fuel or battery charge required to complete theroute, to which an additional buffer amount may be added for error,delay, or travel to a fueling or charging station upon arrival. Forautonomous electric vehicles, excessive recharging of mostly fullbatteries may also decrease the useful life of the batteries, which themaximum threshold helps avoid. If the fuel level or battery charge levelis determined to be at or above the maximum threshold, the method 900may terminate without refueling or recharging the vehicle 108.Otherwise, the method 900 may continue.

At block 910, the on-board computer 114 may determine whether there issufficient time to refuel or recharge the vehicle 108 based upon thepredicted future use. This may be determined based in part upon traveltime, as well as refueling or recharging time. A current location of thevehicle 108 (or a predicted location of the vehicle 108 at an expectedstopping point for a break during vehicle operation) may be identifiedand used to estimate travel time. The fuel level or battery charge levelmay be used to determine refueling or recharging time. Particularly forautonomous electric vehicles, the recharging time may be significant andmay vary considerably based upon the remaining charge of the battery. Insome embodiments, the predicted use profile may be used to determinewhether the next predicted use of the vehicle allows sufficient time fortravel to a refueling or recharging station, refueling or recharging,and return travel. Where the predicted use profile includesprobabilities of use, the combined probability over the time requiredfor refueling or recharging may be compared against a probabilitythreshold to determine whether it is sufficiently unlikely that avehicle operator will want to use the vehicle 108 over the time period.If sufficient time is determined to exist, the on-board computer 114 mayproceed with refueling or recharging at blocks 914-924.

In some embodiments, the on-board computer 114 may send a notificationor request for confirmation to one or more vehicle operators (or mobiledevices 110 associated with the vehicle operators) via the network 130to obtain confirmation that sufficient time exists to refuel or rechargethe vehicle 108 before the next use. The notification or request mayinclude an estimated duration of vehicle unavailability based upontravel time and refueling or recharging time. The on-board computer 114may wait until a confirmatory response is received to proceed withrefueling or recharging in some embodiments.

If sufficient time is determined not to exist for refueling orrecharging (or if no response is received from the vehicle operator),the on-board computer 114 may determine whether refueling or rechargingis nonetheless necessary. At block 912, the on-board computer 114 maydetermine whether the fuel level or battery charge level is below aminimum threshold. Such minimum threshold level may be set by thevehicle operator, may indicate a minimum reserve beyond which vehicleoperation is discouraged, or may be determined as the minimum fuel orbattery charge required to reach a fueling or charging station. When thefuel level or battery charge level is below the minimum threshold, theon-board computer 114 may proceed with refueling or recharging at blocks914-924, even though such refueling may interfere with predicted vehicleuse because the predicted vehicle use would be infeasible with thecurrent fuel or battery charge levels.

At block 914, the on-board computer 114 may identify a time and locationfor refueling or recharging the vehicle 108. The time and location maybe based at least in part upon the predicted future use, such as thepredicted use profile. If the vehicle is not in use, the time may be acurrent time at which the time and location are determined. If thevehicle is in use, the time and location may be determined based upon apredicted break in operation using the predicted use profile. When thevehicle 108 is refueled or recharged during a break in a vehicle trip,the on-board computer 114 may wait to identify the location until thebreak begins. In either case, the location may be identified as alocation associated with a fueling or charging station and may furtherbe based upon fueling or charging station availability. Suchavailability may be determined by automatic electronic communication viathe network 130 between the on-board computer 114 and a remote serverassociated with the fueling or charging station. In some embodiments,the location of the fueling or charging station may be determined basedupon geospatial location data associated with a current or futurelocation of the vehicle 108. In some such embodiments, the on-boardcomputer 114 may obtain GPS data from a GPS unit 206 indicating acurrent location of the vehicle 108, which may be used to identify oneor more fueling or charging stations in proximity to the currentlocation (i.e., within a linear distance, a travel distance, or a traveltime). The one or more fueling or charging stations may be identified byaccessing a database of stations, which may be stored locally in aprogram memory 208 or may be a database 146 associated with a server 140and accessed via the network 130. One of the identified fueling orcharging stations may be selected as the location for refueling orrecharging based upon distance or time from the current location. Once alocation and time for refueling or recharging are determined, a routemay be determined and used to control the vehicle 108 to the fueling orcharging station.

At block 916, the on-board computer 114 may obtain data indicating afully autonomous route between a current location and the identifiedrefueling or recharging location. The route may be obtained from a mapserver 140 via the network 130 or may be determined by the on-boardcomputer 114 or mobile device 110 within the vehicle 108 based upon mapdata indicating suitability for autonomous operation for a plurality ofroad segments. The map data may be accessed to obtain informationregarding the plurality of road segments to identify a fully autonomousroute (i.e., a route the vehicle 108 can traverse using only fullyautonomous operation, without manual or semi-autonomous operation)between the current location and the refueling or recharging location,using the routing methods described elsewhere herein. Once the routedata is obtained, at block 918, the on-board computer 114 may controlthe vehicle 108 to travel along the road segments of the route in afully autonomous mode between the current location and the refueling orrecharging location.

At block 920, the on-board computer 114 may cause the vehicle 108 torefuel or recharge upon reaching the refueling or recharging location.Causing the vehicle 108 to refuel or recharge may include causing thevehicle 108 to maneuver to a location in proximity to a refueling orrecharging station and automatically electronically communicating withthe refueling or recharging station to begin refueling or recharging. Insome embodiments, this may include sending a message to the station, inresponse to which an attendant at the station may refuel the vehicle108. In other embodiments, this may include directly communicating witha refueling or recharging device to automatically connect the vehicle108 to the refueling or recharging device. Such connection may beeffected via a direct physical connection (e.g., a tube or wire), or itmay be a wireless connection for electric vehicle recharging. Oncerefueling or recharging is complete, the vehicle 108 may return to areturn location. In some embodiments, refueling or recharging may bestopped prior to completion by a vehicle operator recall of the vehicle108, such as by a command from a mobile device 110 of the vehicleoperator.

At block 922, the on-board computer 114 may obtain data indicating afully autonomous route between the refueling or recharging location to areturn location. Such return route data may be obtained or determined ina manner similar to that described above.

The return location may be the same location from which the vehicle 108began its autonomous travel to the refueling or recharging location, orthe return location may be a distinct location. For example, the vehicle108 may travel from a drop-off location near an entrance to a buildingbut may return to a parking space in a remote garage, where the vehicle108 may park until recalled by the vehicle operator. The return locationmay be determined based upon the predicted use profile or vehicleoperator information, such as locations of one or more vehicle operatorsobtained from mobile devices 110 associated with the vehicle operators.Once the return route data is obtained, at block 924, the on-boardcomputer 114 may control the vehicle 108 to travel along the roadsegments of the return route in a fully autonomous mode between therefueling or recharging location and the return location. Upon arrivingat the return location, the vehicle 108 may park, and the method 900 mayterminate.

Automatic Passive Searching

In addition to being used in vehicle control, the sensors 120 withinautonomous vehicles 108 may be used to provide information about theirsurroundings. This data regarding the vehicle environment may be used topassively search for vehicles, people, animals, objects, or other itemswithin the vehicle environment without action by vehicle operators orpassengers. By remotely activating sensors 120 or triggering analysis ofsensor data being generated for other purposes by a plurality ofvehicles, wide areas can be more quickly and effectively searched. Thismay include causing a plurality of vehicles 108 in an area of interestto automatically obtain and evaluate sensor data based upon searchcriteria associated with a vehicle, person, or other item of interest.Such automatic searching may be performed at each of the plurality ofvehicle in response to information received from a remote server 140 viaa network 130, particularly via wireless communication over at least aportion of the network 130. The automatic searching may be performedwithout notifying or alerting the vehicle operators or passengers of thevehicles 108 by running the evaluation processes in the backgroundduring vehicle operation, which may require prior authorization by thevehicle operator or owner. Parked vehicles or other vehicles notcurrently operating may also be caused to collect and evaluate sensordata in some embodiments.

The methods of automatic passive searching by autonomous vehiclesdescribed herein improve upon the existing methods of emergency alerts,such as Amber Alerts™ or emergency bulletins released by law enforcementagencies. Existing methods require active searching by human assessmentof items within their environment, which places a cognitive burden onvehicle operators or passengers. This disadvantage of current techniquesis compounded by the need for the human searchers to learn informationabout the search, such as by receiving information via a mobile device110. Use of such devices is restricted or prohibited while operatingvehicles in many situations, and such use is impractical in manyadditional situations. Even where permissible and practicable, suchnotification techniques of notifying human searchers suffer delay,limited compliance, and human sensory and data processing limitations.Moreover, human search techniques inherently distract vehicle operators(both while receiving an alert while driving and while scanning theenvironment for vehicles or people while driving), which increasesaccident risk in the area of search. The methods described hereinimprove upon the existing techniques by allowing passive searching byautonomous vehicle sensors, which can be performed without theintervention or knowledge of the vehicle operator and using excessprocessing capacity of an on-board computer 114 or mobile device 110.

FIG. 10 illustrates a flow diagram of an exemplary passive searchingmethod 1000 for automatically searching an area for vehicles, people, orother items using sensor data from a plurality of autonomous vehicles.The passive searching method 1000 may begin upon receiving an indicationof passive search parameters at a server 140 (block 1002), which mayinclude information regarding a situation triggering the search. Theserver 140 may then identify a plurality of vehicles in a search areabased upon the passive search parameters (block 1004), which may includereceiving location information from a plurality of vehicles. The server140 may then generate and send an indication of search criteria to eachof the identified vehicles 108 (block 1006). The search criteria mayinclude information regarding the search to be performed, such asinformation regarding one or more target items (e.g., a person orvehicle sought). Upon receiving the indication of search criteria, acontroller 204 of an on-board computer 114 or mobile device 110 withinthe vehicle 108 may collect data from one or more sensors 120 regardingthe vehicle's environment (block 1008). The sensor data may then beevaluated by the controller 204 based upon the search criteriaassociated with the received indication of search criteria (block 1010).When at least part of the sensor data is determined to meet the searchcriteria (block 1012), the controller 204 may send a response to theserver 140 via the network 130 indicating the match with the searchcriteria (block 1014). Otherwise, the controller 204 may continue tocollect and evaluate sensor data until the controller 204 determines todiscontinue passively searching for the search criteria (block 1016). Insome embodiments, additional, alternative, or fewer actions may beperformed, and the actions may be performed by various components of thesystem 100 in various combinations.

At block 1002, the server 140 may receive an indication of one or morepassive search parameters. The indication may be received from athird-party server or from a third-party server (e.g., a server operatedby an emergency service provider) or from a user device (e.g., aworkstation or an input device). The indication may include informationregarding a situation that necessitates the search, as well as thepassive search parameters defining a scope of the search (i.e., alocation and a target identification or description). For example, theindication may be received from a law enforcement agency or a privatesecurity firm as a missing person alert, a missing animal alert, astolen vehicle alert, a stolen equipment alert, a person of interestalert, or a fugitive person alert. Such alerts may include informationregarding a location, such as last known locations or likelydestinations or routes. Such alerts may also include informationregarding an identity or description of a target (e.g., a person orvehicle of interest), such as identities or descriptions of persons ofinterest, license plate numbers or descriptions of vehicles (i.e., make,model, color, and/or condition of a vehicle of interest), images ofpersons or vehicles, biometric data associated with persons of interest.A time associated with the triggering situation, a type of thesituation, or other similar information that may indicate parameters ofthe passive search may also be included.

In some embodiments, the passive search parameters may be directlyreceived in a message or via direct user input. Alternatively, theserver 140 may determine one or more passive search parameters basedupon the other information received in the indication of the passivesearch parameters. For example, a search area may be included within theindication (e.g., by specifying a city, county, or other geographical orpolitical subdivision), or a search area may be determined by the server140 based upon the a location and time associated with the situation(e.g., by determining a radius around the location based upon an assumedspeed of travel and the time elapsed). Similarly, biometric dataregarding one or more persons of interest may be directly received ormay be obtained by the server 140 based upon identifying informationregarding the persons, such as by accessing the biometric data from agovernmental or other database. Relevant biometric data may includefacial features, height, weight, sex, voice, tattoos, otherdistinguishing features that may be used to identify a person.

At block 1004, the server 140 may identify a plurality of vehicles 108within the search area received in or based upon the information of theindication of the passive search parameters. To determine the pluralityof vehicles within the search area, the server 140 may define the searcharea in terms of GPS coordinates (e.g., as GPS coordinate ranges). GPSlocation data may then be obtained or accessed for a plurality ofvehicles including the plurality of vehicles within the search area anda plurality of additional vehicles outside the search area. The GPSlocation data may be obtained by polling the vehicles or may be receivedwithout request from the vehicles (e.g., periodically) from a GPS unit206 of each vehicle. The GPS location data may then be compared with thesearch area to identify the plurality of vehicles within the searcharea. In some embodiments, the plurality of vehicles within the searcharea may further be determined based upon sensor and/or processingcapabilities of the vehicles based upon the passive search parameters,such as by limiting the plurality of vehicles to vehicles withsufficiently detailed cameras to accurately perform the search. In someembodiments, the determination may include vehicles that are notcurrently in the search area but are predicted to enter the search areawithin a relevant search time period based upon route informationassociated with the vehicles. For example, the server 140 may haveaccess to route information for the plurality of vehicles, which may beevaluated to determine whether the vehicles will enter or exit thesearch area over a relevant time period.

At block 1006, the server 140 may generate and send an indication ofsearch criteria to the identified plurality of vehicles 108 via thenetwork 130. The indication of the search criteria may be received by acontroller 204 of the vehicle 108, which may be disposed within anon-board computer 114 or a mobile device 110 within the vehicle 108. Insome embodiments, the search criteria may be identical to the passivesearch parameters received or determined by the server 140.Alternatively, the search criteria may be determined or derived by theserver 140 from the passive search parameters. For example, the searchcriteria may specifically describe data to be matched or criteria to bemet by the sensor data of the vehicle 108, whereas the passive searchparameters may be more general. To determine the search criteria, theserver 140 may access one or more lookup tables to determine specificdata criteria associated with the passive search parameters. The searchcriteria may be expressly included within the indication, or theindication may include a reference from which the computing systems ofthe vehicle 108 may determine the search criteria. For example, theindication of the search criteria may include a reference to a make,model, and color of a target vehicle of interest, which the controller204 of the vehicle 108 may use to look up information specifyingdimensions of the target vehicle based upon the received indication.

Upon receiving the indication of the search criteria, each vehicle 108of the identified plurality of vehicles may begin monitoring sensor datafrom one or more sensors 120 based upon the received search criteria.The controller 204 of each vehicle 108 may perform the actions of blocks1008-1016 until the controller 204 determines to discontinue searchingat block 1016. In a preferred embodiment, the actions of blocks1008-1016 may be performed without notifying or alerting vehiclesoccupants (i.e., vehicle operators or passengers) of the receipt of theindication of the search criteria or of the performance of such sensordata collection and evaluation. In other embodiments, a notification maybe provided to the vehicle operator if desired.

At block 1008, the controller 204 may obtain sensor data by using one ormore sensors 120 of the vehicle 108 to collect information regarding thevehicle environment in which the vehicle 108 is located. If the vehicle108 is currently operating, the controller 204 may access sensor datacollected by the sensors 120 for other purposes, such as sensor datagenerated by the sensors 120 for one or more autonomous operationfeatures of the vehicle 108. For example, image data may be generated byone or more cameras or other image generation devices for vehiclesteering and collision avoidance. Such image data may be obtained by thecontroller 204 and used for passive searching, as well. If the vehicle108 is not currently operating or a sensor 120 is not activelygenerating data the controller 204 may activate the sensors 120 to begingenerating sensor data. For example, controllers 204 of parked vehicles108 within the search area may activate some sensors 120 to passivelymonitor the environment based upon the search criteria even though thevehicles 108 are not in use. For such monitoring, the sensors used maybe limited to sensors 120 that meet energy efficiency requirements, inorder to avoid draining the vehicle's battery. Sensor data may also becollected only periodically from vehicles not in use, thereby furtherreducing the energy used in monitoring the vehicle environment.

At block 1010, the controller 204 may evaluate the sensor data basedupon the search criteria to determine whether the sensor data meets thesearch criteria. The sensor data may be processed to identify matcheswith search criteria, such as vehicle identifying data or biometric dataidentifying a person of interest. For example, the controller 204 mayevaluate image data to determine license plate numbers for othervehicles within the environment of the vehicle 108, which license platenumbers can then be compared against a license plate number indicated bythe search criteria. Similarly, the controller 204 may evaluate imagedata, sound data, or radar data to determine biometric features of apedestrian or other person within the vehicle environment. For example,the controller 204 may use image data to identify a driver of anothernearby vehicle based upon facial features.

The sensor data may also be evaluated by the controller 204 to identifyother nearby vehicles, equipment, or items within the vehicleenvironment. For example, the controller 204 may identify a nearbyvehicle of the make, model, and color of the target vehicle of interestfrom the sensor data. As another example, bicycles or constructionequipment may be identified from the sensor data. Animals may similarlybe identified by the controller 204 from the sensor data, such asdangerous wild animals (e.g., bears or cougars) or missing domesticanimals. To facilitate determining whether the sensor data meets thesearch requirements, the controller 204 may determine one or moreintermediate items describing aspects of objects within the vehicleenvironment (e.g., license plate numbers, estimated vehicle types,estimated facial feature profiles, estimated heights and weights ofpersons, etc.). Such intermediate items may then be compared with thesearch criteria to determine whether the associated items meet thesearch criteria.

At block 1012, the controller 204 may determine whether the sensor datamatches the search criteria. In some embodiments, this may includedetermining whether each of a plurality of intermediate informationitems meets one or more distinct criteria of the search criteria. Forexample, the controller 204 may determine whether a license plate numberof a nearby vehicle determined from sensor data matches a license platenumber of the search criteria. Similar determinations may be made forbiometric data (e.g., facial features, height, weight, distinguishingfeatures, etc.), vehicle descriptions (e.g., make, model, color,condition, etc.), equipment descriptions, or other types of criteria.

When the sensor data or intermediate information derived therefrom meetsthe search criteria (or a sufficient portion thereof), the controller204 may generate and send a response message to the server 140 via thenetwork 130 at block 1014. The response message may include the relevantsensor data or an indication or summary thereof. The response messagemay also include the location of the vehicle 108 and a time associatedwith the sensor data meeting the search criteria. Upon receivingresponse messages from one or more of the vehicles 108, the server 140may implement an appropriate action based upon the information in theresponse message. Such action may include communicating the responsemessage, sensor data, and/or other relevant information to a third-partyserver associated with a law enforcement agency or private securityfirm. In some embodiments, the server 140 may identify a nearby vehicle108 (which may be a different vehicle than the one or more vehicles fromwhich the response message was received) to follow a target vehicle orperson of interest. Such action may include communicating a controlmessage to the nearby vehicle 108 via the network 130 to cause thevehicle 108 to identify and follow the target vehicle or person ofinterest at a distance.

When the sensor data or intermediate information does not meet asufficient portion of the search criteria, the controller 204 maydetermine whether to continue or discontinue the passive search at block1016. The controller 204 may determine to discontinue the passive searchafter a duration of time, which may be included in the indication of thesearch criteria. The controller 204 may also determine to discontinuethe passive search if the vehicle 108 leaves the search area, which mayalso be included in the indication of the search criteria. In someembodiments, the controller 204 may determine to discontinue the passivesearch when the vehicle 108 is parked and shut down at the end of atrip, ceasing operation for a period of time. In some embodiments, thecontroller 204 may also discontinue operation in response to receiving asearch cancellation message from the server 140 via the network 130,which the server 140 may generate and communicate when sufficientresponse messages are received or after a predetermined period of timehas elapsed.

Exemplary Methods of Determining Risk Using Telematics Data

As described herein, telematics data may be collected and used inmonitoring, controlling, evaluating, and assessing risks associated withautonomous or semi-autonomous operation of a vehicle 108. In someembodiments, the Data Application installed on the mobile computingdevice 110 and/or on-board computer 114 may be used to collect andtransmit data regarding vehicle operation. This data may includeoperating data regarding operation of the vehicle 108, autonomousoperation feature settings or configurations, sensor data (includinglocation data), data regarding the type or condition of the sensors 120,telematics data regarding vehicle regarding operation of the vehicle108, environmental data regarding the environment in which the vehicle108 is operating (e.g., weather, road, traffic, construction, or otherconditions). Such data may be transmitted from the vehicle 108 or themobile computing device 110 via radio links 183 (and/or via the network130) to the server 140. The server 140 may receive the data directly orindirectly (i.e., via a wired or wireless link 183 e to the network 130)from one or more vehicles 182 or mobile computing devices 184. Uponreceiving the data, the server 140 may process the data to determine oneor more risk levels associated with the vehicle 108.

In some embodiments, a plurality of risk levels associated withoperation of the vehicle 108 may be determined based upon the receiveddata, using methods similar to those discussed elsewhere herein, and atotal risk level associated with the vehicle 108 may be determined basedupon the plurality of risk levels. In other embodiments, the server 140may directly determine a total risk level based upon the received data.Such risk levels may be used for vehicle navigation, vehicle control,control hand-offs between the vehicle and driver, settings adjustments,driver alerts, accident avoidance, insurance policy generation oradjustment, and/or other processes as described elsewhere herein.

In some aspects, computer-implemented methods for monitoring the use ofa vehicle 108 having one or more autonomous operation features and/oradjusting an insurance policy associated with the vehicle 108 may beprovided. Such methods may comprise the following, with the customer'spermission or affirmative consent: (1) collecting sensor data regardingoperation of the vehicle 108 from one or more sensors 120 of a mobilecomputing device 110 and/or otherwise disposed within the vehicle 108;(2) determining telematics data regarding operation of the vehicle 108based upon the collected sensor data by the mobile computing device 110and/or on-board computer 114; (3) determining feature use levelsindicating usage of the one or more autonomous operation features duringoperation of the vehicle 108 by an on-board computer of the vehicle 114;(4) receiving the determined feature use levels from the on-boardcomputer 114 at the mobile computing device 110; (5) transmittinginformation including the telematics data and the feature use levelsfrom the mobile computing device 114 and/or a communication component122 of the vehicle 108 to a remote server 140 via a radio link 183 orwireless communication channel; (6) receiving the telematics data andthe feature use levels at one or more processors of the remote server140; and/or (7) determining a total risk level associated with operationof the vehicle 108 based at least in part upon the received telematicsdata and feature use levels by one or more processors of the remoteserver 140. The remote server 140 may receive the information through acommunication network 130 that includes both wired and wirelesscommunication links 183.

In some embodiments, the mobile computing device 110 and/or on-boardcomputer 114 may have a Data Application installed thereon, as describedabove. Such Data Application may be executed by one or more processorsof the mobile computing device 110 and/or on-board computer 114 to, withthe customer's permission or affirmative consent, collect the sensordata, determine the telematics data, receive the feature use levels, andtransmit the information to the remote server 140. The Data Applicationmay similarly perform or cause to be performed any other functions oroperations described herein as being controlled by the mobile computingdevice 110 and/or on-board computer 114.

The telematics data may include data regarding one or more of thefollowing regarding the vehicle 108: acceleration, braking, speed,heading, and/or location. The telematics data may further includeinformation regarding one or more of the following: time of day ofvehicle operation, road conditions in a vehicle environment in which thevehicle is operating, weather conditions in the vehicle environment,and/or traffic conditions in the vehicle environment. In someembodiments, the one or more sensors 120 of the mobile computing device110 may include one or more of the following sensors disposed within themobile computing device 110: an accelerometer array, a camera, amicrophone, and/or a geolocation unit (e.g., a GPS receiver). In furtherembodiments, one or more of the sensors 120 may be communicativelyconnected to the mobile computing device 110 (such as through a wirelesscommunication link).

The feature use levels may be received by the mobile computing device110 from the on-board computer 114 via yet another radio link 183between the mobile computing device 110 and the on-board computer 114,such as link 116. The feature use levels may include data indicatingadjustable settings for at least one of the one or more autonomousoperation features. Such adjustable settings may affect operation of theat least one of the one or more autonomous operation features incontrolling an aspect of vehicle operation, as described elsewhereherein.

In some embodiments, the method may further including receivingenvironmental information regarding the vehicle's environment at themobile computing device 110 and/or on-board computer 114 via anotherradio link 183 or wireless communication channel. Such environmentalinformation may also be transmitted to the remote server 140 via theradio link 183 and may be used by the remote server 140 in determiningthe total risk level. In some embodiments, the remote server 140 mayreceive part or all of the environmental information through the network130 from sources other than the mobile computing device 110 and/oron-board computer 114. Such sources may include third-party datasources, such as weather or traffic information services. Theenvironmental data may include one or more of the following: roadconditions, weather conditions, nearby traffic conditions, type of road,construction conditions, location of pedestrians, movement ofpedestrians, movement of other obstacles, signs, traffic signals, oravailability of autonomous communications from external sources. Theenvironmental data may similarly include any other data regarding avehicle environment described elsewhere herein.

In further embodiments, the method may include collecting additiontelematics data and/or information regarding feature use levels at aplurality of additional mobile computing devices 184 associated with aplurality of additional vehicles 182. Such additional telematics dataand/or information regarding feature use levels may be transmitted fromthe plurality of additional mobile computing devices 184 to the remoteserver 140 via a plurality of radio links 183 and receive at one or moreprocessors of the remote server 140. The remote server 140 may furtherbase the determination of the total risk level at least in part upon theadditional telematics data and/or feature use levels.

Some embodiments of the methods described herein may includedetermining, adjusting, generating, rating, or otherwise performingactions necessary for creating or updating an insurance policyassociated with the vehicle 108. Thus, the remote server 140 may receivea request for a quote of a premium associated with a vehicle insurancepolicy associated with the vehicle 108. Such request may be transmittedvia the network 130 from the mobile computing device 110 or anothercomputing device associated with an insurance customer. Alternatively,such request may be generated upon the occurrence of an event, such asthe passage of time or a change in a risk level associated withoperation of the vehicle 108. In some embodiments, a routine executingon the sever 140 may generate the request based upon the occurrence ofan event. Upon receiving such request, the remote server 140 maydetermine a premium associated with the vehicle insurance policy basedat least in part upon the total risk level. An option to purchase thevehicle insurance policy may be presented to a customer associated withthe vehicle 108, or information regarding an (actual or predicted)adjustment to an insurance policy may be presented to the customer. Forexample, the server 140 may cause a predicted change to an insurancepolicy (e.g., an increase or decrease in a premium) to be presented tothe vehicle operator, such as when the vehicle operator is adjustingautonomous operation feature settings. The remote server 140 mayalternatively, or additionally, provide information regarding thepremium, coverage levels, costs, discounts, rates, or similarinformation associated with the insurance policy to be presented to thecustomer for review and/or approval by the mobile computing device 110or another computing device associated with the customer.

Risk Assessment

The present embodiments may relate to risk assessment and insurancepremium calculation. Autonomous software data may be analyzed to measurethe risks of transitioning between human and vehicle as the driver(which may vary by driving environment, e.g., transitioning on thehighway, when approaching construction, when exiting the highway, whenthe driver becomes impaired, and when the driver becomes distracted).Accidents related to the transition of control between the driver andthe vehicle may become a common cause of accidents for autonomousvehicles. An insurance provider may be able to provide users informationabout instances when the user resumed control too late, or disengagedtoo soon, in order to help users transfer control more safely and reducethe risk of future accidents. Insurance provider remote servers may alsobe able to notify users of instances in which they themselves or otherhuman drivers have activated autonomous driving features in drivingenvironments for which the technology was not intended, such as usingautonomous highway driving features on narrow country roads whenintended for use only on divided highways.

An assessment may be performed that compares a vehicle's autonomouscapabilities against how drivers are using the features. The presentembodiments may be configured to measure when an autonomous vehicle isin control, when the driver is in control, neither, or both. The timeswhen both the driver and the vehicle have partial or joint control mayalso be determined and measured. These times may present higher risk,and an appropriate auto insurance premium may be higher based upon thenumber of instances of partial or joint control (or partial lack ofcontrol), i.e. the frequency of control transitions. Based upon how theautonomous vehicle software handles these partial or joint controlsituations, premiums or discounts may be adjusted accordingly based uponrisk.

The present embodiments may also be associated with unit-based costs(e.g., per-mile or per-minute premiums) that may only charge fornon-autonomous driving or charge a flat fee plus non-autonomous drivingfactor or fee. For instance, a vehicle manufacturer's policy may coverautonomous driving liability, and manual driving liability forindividual customers may be covered via a personal liability policy. Itis noted that a personal liability policy may have a lower premiumbecause of commercial policy coverage. An insurance policy may be usedto define autonomous driving. Autonomous vehicle data may be analyzed todetermine liability for individual claims. Data, such as sensor orsystem data, may include whether a customer performed requiredmaintenance, and/or met responsibilities defined by an originalequipment manufacturer (OEM). Insurance policies may state that if aloss is not covered by the OEM, the insurance provider policy will coverthe loss (i.e., the insurance provider provides “gap” coverage). Also, acommercial may cover the OEM, software developer, and/or hardwaredeveloper only when vehicle is operating in autonomous mode policy(e.g., product liability). Autonomous vehicle data may be analyzed todetermine liability for a claim, including whether a customer performedrequired maintenance, met responsibilities defined by OEM, and/or whatcomponents were involved in leading to or causing a vehicle collision.Insurance premiums for product liability for an autonomous system mayonly be charged to the customer when the autonomous system is used. Forinstance, a supplier that sells adaptive cruise control systems ortechnology may only be charged for product liability when the adaptivecruise control systems or technology are being used—similar tousage-based insurance, whether usage is measured by miles or operationaltime. The present embodiments may also provide non-insurance based uses.Road coverage maps may be developed or generated for various autonomousvehicle software programs. Users may be able to view which autonomousvehicles work in their city, and/or for their typical daily commute.Autonomous vehicles may also be used to scan license plates for policealerts, stolen vehicles, etc.

Autonomous Vehicle Insurance

The disclosure herein relates in part to insurance policies for vehicleswith autonomous operation features. Accordingly, as used herein, theterm “vehicle” may refer to any of a number of motorized transportationdevices. A vehicle may be a car, truck, bus, train, boat, plane,motorcycle, snowmobile, other personal transport devices, etc. Also asused herein, an “autonomous operation feature” of a vehicle means ahardware or software component or system operating within the vehicle tocontrol an aspect of vehicle operation without direct input from avehicle operator once the autonomous operation feature is enabled orengaged. Autonomous operation features may include semi-autonomousoperation features configured to control a part of the operation of thevehicle while the vehicle operator controls other aspects of theoperation of the vehicle.

The term “autonomous vehicle” means a vehicle including at least oneautonomous operation feature, including semi-autonomous vehicles. A“fully autonomous vehicle” means a vehicle with one or more autonomousoperation features capable of operating the vehicle in the absence of orwithout operating input from a vehicle operator. Operating input from avehicle operator excludes selection of a destination or selection ofsettings relating to the one or more autonomous operation features.Autonomous and semi-autonomous vehicles and operation features may beclassified using the five degrees of automation described by theNational Highway Traffic Safety Administration's. An “electric vehicle”means a vehicle using stored electrical energy to generate motive forceto propel the vehicle using an electric motor. An “autonomous electricvehicle” means an autonomous vehicle that is also an electric vehicle.

Additionally, the term “insurance policy” or “vehicle insurance policy,”as used herein, generally refers to a contract between an insurer and aninsured. In exchange for payments from the insured, the insurer pays fordamages to the insured which are caused by covered perils, acts, orevents as specified by the language of the insurance policy. Thepayments from the insured are generally referred to as “premiums,” andtypically are paid by or on behalf of the insured upon purchase of theinsurance policy or over time at periodic intervals. Although insurancepolicy premiums are typically associated with an insurance policycovering a specified period of time, they may likewise be associatedwith other measures of a duration of an insurance policy, such as aspecified distance traveled or a specified number of trips. The amountof the damages payment is generally referred to as a “coverage amount”or a “face amount” of the insurance policy. An insurance policy mayremain (or have a status or state of) “in-force” while premium paymentsare made during the term or length of coverage of the policy asindicated in the policy. An insurance policy may “lapse” (or have astatus or state of “lapsed”), for example, when the parameters of theinsurance policy have expired, when premium payments are not being paid,when a cash value of a policy falls below an amount specified in thepolicy, or if the insured or the insurer cancels the policy.

The terms “insurer,” “insuring party,” and “insurance provider” are usedinterchangeably herein to generally refer to a party or entity (e.g., abusiness or other organizational entity) that provides insuranceproducts, e.g., by offering and issuing insurance policies. Typically,but not necessarily, an insurance provider may be an insurance company.The terms “insured,” “insured party,” “policyholder,” and “customer” areused interchangeably herein to refer to a person, party, or entity(e.g., a business or other organizational entity) that is covered by theinsurance policy, e.g., whose insured article or entity is covered bythe policy. Typically, a person or customer (or an agent of the personor customer) of an insurance provider fills out an application for aninsurance policy. In some cases, the data for an application may beautomatically determined or already associated with a potentialcustomer. The application may undergo underwriting to assess theeligibility of the party and/or desired insured article or entity to becovered by the insurance policy, and, in some cases, to determine anyspecific terms or conditions that are to be associated with theinsurance policy, e.g., amount of the premium, riders or exclusions,waivers, and the like. Upon approval by underwriting, acceptance of theapplicant to the terms or conditions, and payment of the initialpremium, the insurance policy may be in-force, (i.e., the policyholderis enrolled).

Although the exemplary embodiments discussed herein relate to automobileinsurance policies, it should be appreciated that an insurance providermay offer or provide one or more different types of insurance policies.Other types of insurance policies may include, for example, commercialautomobile insurance, inland marine and mobile property insurance, oceanmarine insurance, boat insurance, motorcycle insurance, farm vehicleinsurance, aircraft or aviation insurance, and other types of insuranceproducts.

Some embodiments described herein may relate to assessing and pricinginsurance based upon autonomous (or semi-autonomous) operation of thevehicle 108. Risk levels and/or insurance policies may be assessed,generated, or revised based upon the use of autonomous operationfeatures or the availability of autonomous operation features in thevehicle 108. Additionally, risk levels and/or insurance policies may beassessed, generated, or revised based upon the effectiveness oroperating status of the autonomous operation features (i.e., degree towhich the features are operating as intended or are impaired, damaged,or otherwise prevented from full and ordinary operation), location(e.g., general areas, types of areas, or specific road segments) orduration (e.g., distance, time duration of operation, time of day,continuous operation, etc.) of autonomous operation feature use, whetherrecommendations for appropriate feature use or optimal routes arefollowed, or other information associated with the methods describedherein. In particular, compliance, noncompliance, or degree ofcompliance with recommendations or requirements of allowable or optimalroutes (including degree of manual or autonomous operation alongportions of such routes) may be used to determine discounts, surcharges,fees, premiums, etc. Thus, information regarding the capabilities oreffectiveness of the autonomous operation features available to be usedor actually used in operation of the vehicle 108 may be used in riskassessment and insurance policy determinations.

Insurance providers currently develop a set of rating factors based uponthe make, model, and model year of a vehicle. Models with better lossexperience receive lower factors, and thus lower rates. One reason thatthis current rating system cannot be used to assess risk for vehiclesusing autonomous technologies is that many autonomous operation featuresvary for the same vehicle model. For example, two vehicles of the samemodel may have different hardware features for automatic braking,different computer instructions for automatic steering, and/or differentartificial intelligence system versions. The current make and modelrating may also not account for the extent to which another “driver,” inthis case the vehicle itself, is controlling the vehicle. The presentembodiments may assess and price insurance risks at least in part basedupon autonomous operation features that replace actions of the driver.In a way, the vehicle-related computer instructions and artificialintelligence may be viewed as a “driver.”

Insurance policies, including insurance premiums, discounts, andrewards, may be updated, adjusted, and/or determined based upon hardwareor software functionality, and/or hardware or software upgrades,associated with autonomous operation features. Insurance policies,including insurance premiums, discounts, etc. may also be updated,adjusted, and/or determined based upon the amount of usage and/or thetype(s) of the autonomous or semi-autonomous technology employed by thevehicle. In one embodiment, performance of autonomous driving softwareand/or sophistication of artificial intelligence utilized in theautonomous operation features may be analyzed for each vehicle. Anautomobile insurance premium may be determined by evaluating howeffectively the vehicle may be able to avoid and/or mitigate crashesand/or the extent to which the driver's control of the vehicle isenhanced or replaced by the vehicle's software and artificialintelligence.

When pricing a vehicle with autonomous operation features, artificialintelligence capabilities, rather than human decision making, may beevaluated to determine the relative risk of the insurance policy. Thisevaluation may be conducted using multiple techniques. Autonomousoperation feature technology may be assessed in a test environment, inwhich the ability of the artificial intelligence to detect and avoidpotential crashes may be demonstrated experimentally. For example, thismay include a vehicle's ability to detect a slow-moving vehicle aheadand/or automatically apply the brakes to prevent a collision.Additionally, actual loss experience of the software in question may beanalyzed. Vehicles with superior artificial intelligence and crashavoidance capabilities may experience lower insurance losses in realdriving situations. Results from both the test environment and/or actualinsurance losses may be compared to the results of other autonomoussoftware packages and/or vehicles lacking autonomous operation featuresto determine a relative risk levels or risk factors for one or moreautonomous operation features. To determine such risk levels or factors,the control decisions generated by autonomous operation features may beassessed to determine the degree to which actual or shadow controldecisions are expected to succeed in avoiding or mitigating vehicleaccidents. This risk levels or factors may be applicable to othervehicles that utilize the same or similar autonomous operation featuresand may, in some embodiments, be applied to vehicle utilizing similarfeatures (such as other software versions), which may require adjustmentfor differences between the features.

Emerging technology, such as new iterations of artificial intelligencesystems or other autonomous operation features, may be priced bycombining an individual test environment assessment with actual lossescorresponding to vehicles with similar autonomous operation features.The entire vehicle software and artificial intelligence evaluationprocess may be conducted with respect to each of various autonomousoperation features, including fully autonomous operation feature,semi-autonomous operation features, or vehicle-to-vehiclecommunications. A risk level or risk factor associated with the one ormore autonomous operation features of the vehicle could then bedetermined and applied when pricing insurance for the vehicle. In someembodiments, the driver's past loss experience and/or other driver riskcharacteristics may not be considered for fully autonomous vehicles, inwhich all driving decisions are made by the vehicle's artificialintelligence. Risks associated with the driver's operation of thevehicle may, however, be included in embodiments in which the drivercontrols some portion of vehicle operation in at least somecircumstances.

In one embodiment, a separate portion of the automobile insurancepremium may be based explicitly on the effectiveness of the autonomousoperation features. The artificial intelligence pricing model may becombined with traditional methods for semi-autonomous vehicle operation.Insurance pricing for fully autonomous, or driverless, vehicles may bebased upon an artificial intelligence model score by excludingtraditional rating factors that measure risk presented by the drivers.Evaluation of vehicle software and/or artificial intelligence may beconducted on an aggregate basis or for specific combinations ofautonomous operation features and/or driving factors.

An analysis of how the artificial intelligence of autonomous operationfeatures facilitates avoiding accidents and/or mitigates the severity ofaccidents in order to build a database and/or model of risk assessment.After which, automobile insurance risk and/or premiums (as well asinsurance discounts, rewards, and/or points) may be adjusted based uponautonomous or semi-autonomous vehicle functionality, such as byindividual autonomous operation features or groups thereof. In oneaspect, an evaluation may be performed of how artificial intelligence,and the usage thereof, impacts automobile accidents and/or automobileinsurance claims. Such analysis may be based upon data from a pluralityof autonomous vehicles operating in ordinary use, or the analysis may bebased upon tests performed upon autonomous vehicles and/or autonomousoperation feature test units.

The types of autonomous or semi-autonomous vehicle-related functionalityor technology implemented by various autonomous operation features mayinclude or be related to the following: (a) fully autonomous(driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V)wireless communication; (d) vehicle-to-infrastructure (and/or viceversa) wireless communication; (e) automatic or semi-automatic steering;(f) automatic or semi-automatic acceleration; (g) automatic orsemi-automatic braking; (h) automatic or semi-automatic blind spotmonitoring; (i) automatic or semi-automatic collision warning; (j)adaptive cruise control; (k) automatic or semi-automatic parking/parkingassistance; (1) automatic or semi-automatic collision preparation(windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m)driver acuity/alertness monitoring; (n) pedestrian detection; (o)autonomous or semi-autonomous backup systems; (p) road mapping systems;(q) software security and anti-hacking measures; (r) theftprevention/automatic return; (s) automatic or semi-automatic drivingwithout occupants; and/or other functionality.

The adjustments to automobile insurance rates or premiums based upon theautonomous or semi-autonomous vehicle-related functionality ortechnology may take into account the impact of such functionality ortechnology on the likelihood of a vehicle accident or collisionoccurring or upon the likely severity of such accident or collision. Forinstance, a processor may analyze historical accident information and/ortest data involving vehicles having autonomous or semi-autonomousfunctionality. Factors that may be analyzed and/or accounted for thatare related to insurance risk, accident information, or test data mayinclude the following: (1) point of impact; (2) type of road; (3) timeof day; (4) weather conditions; (5) road construction; (6) type/lengthof trip; (7) vehicle style; (8) level of pedestrian traffic; (9) levelof vehicle congestion; (10) atypical situations (such as manual trafficsignaling); (11) availability of internet connection for the vehicle;and/or other factors. These types of factors may also be weightedaccording to historical accident information, predicted accidents,vehicle trends, test data, and/or other considerations.

Automobile insurance premiums, rates, discounts, rewards, refunds,points, etc. may be adjusted based upon the percentage of time orvehicle usage that the vehicle is the driver, i.e., the amount of time aspecific driver uses each type of autonomous operation feature. In otherwords, insurance premiums, discounts, rewards, etc. may be adjustedbased upon the percentage of vehicle usage during which the autonomousor semi-autonomous functionality is in use. For example, automobileinsurance risks, premiums, discounts, etc. for an automobile having oneor more autonomous operation features may be adjusted and/or set basedupon the percentage of vehicle usage that the one or more individualautonomous operation features are in use, which may include anassessment of settings used for the autonomous operation features. Insome embodiments, such automobile insurance risks, premiums, discounts,etc. may be further set or adjusted based upon availability, use, orquality of Vehicle-to-Vehicle (V2V) wireless communication to a nearbyvehicle also employing the same or other type(s) of autonomouscommunication features.

Insurance premiums, rates, ratings, discounts, rewards, special offers,points, programs, refunds, claims, claim amounts, etc. may be adjustedfor, or may otherwise take into account, the foregoing functionalities,technologies, or aspects of the autonomous operation features ofvehicles, as described elsewhere herein. For instance, insurancepolicies may be updated based upon autonomous or semi-autonomous vehiclefunctionality; V2V wireless communication-based autonomous orsemi-autonomous vehicle functionality; and/or vehicle-to-infrastructureor infrastructure-to-vehicle wireless communication-based autonomous orsemi-autonomous vehicle functionality.

Machine Learning

Machine learning techniques have been developed that allow parametric ornonparametric statistical analysis of large quantities of data. Suchmachine learning techniques may be used to automatically identifyrelevant variables (i.e., variables having statistical significance or asufficient degree of explanatory power) from data sets. This may includeidentifying relevant variables or estimating the effect of suchvariables that indicate actual observations in the data set. This mayalso include identifying latent variables not directly observed in thedata, viz. variables inferred from the observed data points. In someembodiments, the methods and systems described herein may use machinelearning techniques to identify and estimate the effects of observed orlatent variables such as time of day, weather conditions, trafficcongestion, interaction between autonomous operation features, or othersuch variables that influence the risks associated with autonomous orsemi-autonomous vehicle operation.

Some embodiments described herein may include automated machine learningto determine risk levels, identify relevant risk factors, optimizeautonomous or semi-autonomous operation, optimize routes, determineautonomous operation feature effectiveness, predict user demand for avehicle, determine vehicle operator or passenger illness or injury,evaluate sensor operating status, predict sensor failure, evaluatedamage to a vehicle, predict repairs to a vehicle, predict risksassociated with manual vehicle operation based upon the driver andenvironmental conditions, recommend optimal or preferred autonomousoperation feature usage, estimate risk reduction or cost savings fromfeature usage changes, determine when autonomous operation featuresshould be engaged or disengaged, determine whether a driver is preparedto resume control of some or all vehicle operations, and/or determineother events, conditions, risks, or actions as described elsewhereherein. Although the methods described elsewhere herein may not directlymention machine learning techniques, such methods may be read to includesuch machine learning for any determination or processing of data thatmay be accomplished using such techniques. In some embodiments, suchmachine-learning techniques may be implemented automatically uponoccurrence of certain events or upon certain conditions being met. Useof machine learning techniques, as described herein, may begin withtraining a machine learning program, or such techniques may begin with apreviously trained machine learning program.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data (suchas autonomous vehicle system, feature, or sensor data, autonomousvehicle system control signal data, vehicle-mounted sensor data, mobiledevice sensor data, and/or telematics, image, or radar data) in order tofacilitate making predictions for subsequent data (again, such asautonomous vehicle system, feature, or sensor data, autonomous vehiclesystem control signal data, vehicle-mounted sensor data, mobile devicesensor data, and/or telematics, image, or radar data). Models may becreated based upon example inputs of data in order to make valid andreliable predictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as autonomous system sensor and/or control signal data, and otherdata discuss herein. The machine learning programs may utilize deeplearning algorithms are primarily focused on pattern recognition, andmay be trained after processing multiple examples. The machine learningprograms may include Bayesian program learning (BPL), voice recognitionand synthesis, image or object recognition, optical characterrecognition, and/or natural language processing—either individually orin combination. The machine learning programs may also include naturallanguage processing, semantic analysis, automatic reasoning, and/ormachine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct or a preferredoutput. In unsupervised machine learning, the processing element may berequired to find its own structure in unlabeled example inputs. In oneembodiment, machine learning techniques may be used to extract thecontrol signals generated by the autonomous systems or sensors, andunder what conditions those control signals were generated by theautonomous systems or sensors.

The machine learning programs may be trained with autonomous systemdata, autonomous sensor data, and/or vehicle-mounted or mobile devicesensor data to identify actions taken by the autonomous vehicle before,during, and/or after vehicle collisions; identify who was behind thewheel of the vehicle (whether actively driving, or riding along as theautonomous vehicle autonomously drove); identify actions taken be thehuman driver and/or autonomous system, and under what (road, traffic,congestion, or weather) conditions those actions were directed by theautonomous vehicle or the human driver; identify damage (or the extentof damage) to insurable vehicles after an insurance-related event orvehicle collision; and/or generate proposed insurance claims for insuredparties after an insurance-related event.

The machine learning programs may be trained with autonomous systemdata, autonomous vehicle sensor data, and/or vehicle-mounted or mobiledevice sensor data to identify preferred (or recommended) and actualcontrol signals relating to or associated with, for example, whether toapply the brakes; how quickly to apply the brakes; an amount of force orpressure to apply the brakes; how much to increase or decrease speed;how quickly to increase or decrease speed; how quickly to accelerate ordecelerate; how quickly to change lanes or exit; the speed to take whiletraversing an exit or on ramp; at what speed to approach a stop sign orlight; how quickly to come to a complete stop; and/or how quickly toaccelerate from a complete stop.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data inorder to facilitate making predictions for subsequent data, such thatthe machine learning programs may be trained by inputting sample datasets or certain data into the programs, such as image, vehicletelematics, and/or intelligent home telematics data. The machinelearning programs may include Bayesian program learning (BPL), voicerecognition and synthesis, image or object recognition, opticalcharacter recognition, natural language processing, semantic analysis,and/or automatic reasoning. Models may be created based upon exampleinputs in order to make valid and reliable predictions for novel inputs.

Machine learning techniques may be used to extract the relevant personaland/or driving behavior-related information for drivers fromvehicle-mounted, mobile device-mounted, and/or other sensor data,telematics data, image data, vehicle and GPS data, and/or other data. Inone embodiment, a processing element may be trained by providing it witha large sample of conventional analog and/or digital, still and/ormoving (i.e., video) image data, telematics data, and/or other data ofdrivers with known driving characteristics or driving risk profiles.Such information may include, for example, acceleration, cornering,speed, braking, and other driving characteristics and known risksassociated with those characteristics. Based upon these analyses, theprocessing element may learn how to identify characteristics andpatterns that may then be applied to analyzing sensor data, telematicsdata, image data, vehicle data, autonomous system data, GPS data, and/orother data of new drivers or insurance applicants. For example, theprocessing element may learn to determine the applicant's driving riskprofile from telematics and image data of applicant's driving behavior,may learn to identify low risk or risk averse driving behavior by theapplicant through vehicle operation, and/or may learn to determine suchother information as the applicant's typical area of travel. In anotherembodiment, a processing element may be trained by providing it with alarge sample of conventional analog and/or digital, still and/or moving(i.e., video) image data, and/or other data of roads with knowndefects/obstacles or of known obstacles. The road defects/obstacles maybe include pot holes, detours, construction, pedestrians, parkedvehicles, congestion, traffic, and the known obstacles may includepedestrians, vehicles, construction crews, animals (deer, moose, boars,etc.).

After training, machine learning programs (or information generated bysuch machine learning programs) may be used to evaluate additional data.Such data may be related to tests of new autonomous operation feature orversions thereof, actual operation of an autonomous vehicle, or othersimilar data to be analyzed or processed. The trained machine learningprograms (or programs utilizing models, parameters, or other dataproduced through the training process) may then be used for determining,assessing, analyzing, predicting, estimating, evaluating, or otherwiseprocessing new data not included in the training data. Such trainedmachine learning programs may, thus, be used to perform part or all ofthe analytical functions of the methods described elsewhere herein.

Other Matters

In some aspect, customers may opt-in to a rewards, loyalty, or otherprogram. The customers may allow a remote server to collect sensor,telematics, vehicle, mobile device, and other types of data discussedherein. With customer permission or affirmative consent, the datacollected may be analyzed to provide certain benefits to customers. Forinstance, insurance cost savings may be provided to lower risk or riskaverse customers. Recommendations that lower risk or provide costsavings to customers may also be generated and provided to customersbased upon data analysis. The other functionality discussed herein mayalso be provided to customers in return for them allowing collection andanalysis of the types of data discussed herein.

Although the text herein sets forth a detailed description of numerousdifferent embodiments, it should be understood that the legal scope ofthe invention is defined by the words of the claims set forth at the endof this patent. The detailed description is to be construed as exemplaryonly and does not describe every possible embodiment, as describingevery possible embodiment would be impractical, if not impossible. Onecould implement numerous alternate embodiments, using either currenttechnology or technology developed after the filing date of this patent,which would still fall within the scope of the claims.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term ‘_(——————)’ ishereby defined to mean . . . ” or a similar sentence, there is no intentto limit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based upon any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this disclosureis referred to in this disclosure in a manner consistent with a singlemeaning, that is done for sake of clarity only so as to not confuse thereader, and it is not intended that such claim term be limited, byimplication or otherwise, to that single meaning. Finally, unless aclaim element is defined by reciting the word “means” and a functionwithout the recital of any structure, it is not intended that the scopeof any claim element be interpreted based upon the application of 35U.S.C. § 112(f).

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (code embodied on anon-transitory, tangible machine-readable medium) or hardware. Inhardware, the routines, etc., are tangible units capable of performingcertain operations and may be configured or arranged in a certainmanner. In example embodiments, one or more computer systems (e.g., astandalone, client or server computer system) or one or more hardwaremodules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment may be included in at leastone embodiment. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs forsystem and a method for assigning mobile device data to a vehiclethrough the disclosed principles herein. Thus, while particularembodiments and applications have been illustrated and described, it isto be understood that the disclosed embodiments are not limited to theprecise construction and components disclosed herein. Variousmodifications, changes and variations, which will be apparent to thoseskilled in the art, may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope defined in the appended claims.

The particular features, structures, or characteristics of any specificembodiment may be combined in any suitable manner and in any suitablecombination with one or more other embodiments, including the use ofselected features without corresponding use of other features. Inaddition, many modifications may be made to adapt a particularapplication, situation or material to the essential scope and spirit ofthe present invention. It is to be understood that other variations andmodifications of the embodiments of the present invention described andillustrated herein are possible in light of the teachings herein and areto be considered part of the spirit and scope of the present invention.

While the preferred embodiments of the invention have been described, itshould be understood that the invention is not so limited andmodifications may be made without departing from the invention. Thescope of the invention is defined by the appended claims, and alldevices that come within the meaning of the claims, either literally orby equivalence, are intended to be embraced therein.

It is therefore intended that the foregoing detailed description beregarded as illustrative rather than limiting, and that it be understoodthat it is the following claims, including all equivalents, that areintended to define the spirit and scope of this invention.

What is claimed is:
 1. A computer-implemented method for automaticallyrecharging an autonomous electric vehicle, comprising: detecting, by oneor more sensors disposed within the autonomous electric vehicle, chargeinformation associated with a charge level of a battery of theautonomous electric vehicle; determining, by one or more processors, thecharge level of the battery based upon the charge information;generating, by the one or more processors, a predicted use profile forthe autonomous electric vehicle based upon prior vehicle use data;determining, by the one or more processors, a time and a location atwhich to charge the battery based upon the charge level and thepredicted use profile; controlling, by the one or more processors, theautonomous electric vehicle to travel fully autonomously to thedetermined location at the determined time; causing, by the one or moreprocessors, the battery of the autonomous electric vehicle to charge atthe location; determining, by the one or more processors, a returnlocation for the vehicle based upon the predicted use profile; andcontrolling, by the one or more processors, the autonomous electricvehicle to travel fully autonomously to the return location.
 2. Thecomputer-implemented method of claim 1, wherein: the charge informationis determined when the autonomous electric vehicle is not in use; andthe determined time is a current time at which the time and location aredetermined.
 3. The computer-implemented method of claim 1, furthercomprising: determining, by the one or more processors, that the chargelevel is below a maximum recharging threshold, wherein the time andlocation are determined when the charge level is below the maximumrecharging threshold.
 4. The computer-implemented method of claim 3,wherein: the predicted use profile indicates a next predicted use of theautonomous electric vehicle; and the time and location are determinedwhen sufficient time exists to recharge the battery before the nextpredicted use.
 5. The computer-implemented method of claim 1, wherein:the charge information is determined when the autonomous electricvehicle is in use; the predicted use profile includes one or morepredicted breaks in vehicle operation, each predicted break beingassociated with a break time and a break location; and the time andlocation are determined based upon the one or more predicted breaks. 6.The computer-implemented method of claim 1, wherein the location atwhich to charge the battery is associated with a charging stationselected from a plurality of charging stations based at least in partupon availability of the selected charging station.
 7. Thecomputer-implemented method of claim 1, wherein the return location isdetermined based upon the predicted use profile and is distinct from aprior location from which the autonomous electric vehicle travels to thelocation at which to charge the battery.
 8. The computer-implementedmethod of claim 1, further comprising: identifying, using one or moregeolocation components within the autonomous electric vehicle, a currentlocation of the autonomous electric vehicle; and identifying, by the oneor more processors, one or more charging stations in an area surroundingthe current location from a database including location data for aplurality of charging stations, wherein the location at which to chargethe battery is selected from the location data associated with the oneor more charging stations based at least in part upon distance from thecurrent location.
 9. The computer-implemented method of claim 8, furthercomprising: accessing, by the one or more processors, map datacontaining map information regarding a plurality of road segments, themap information including location data associated with each roadsegment and an indication of suitability for autonomous operationfeature use associated with each road segment; and identifying, by theone or more processors, a route consisting of one or more road segmentsfrom the plurality of road segments between the current location and thelocation at which to charge the battery, wherein controlling theautonomous electric vehicle to travel fully autonomously to thedetermined location includes controlling the autonomous electric vehiclealong the identified route.
 10. The computer-implemented method of claim1, wherein the predicted use profile indicates a plurality of useperiods and use locations over at least one day.
 11. A computer systemfor automatically recharging an autonomous electric vehicle, comprising:one or more processors disposed within the autonomous electric vehicle;one or more sensors disposed within the autonomous electric vehicle andcommunicatively connected to the one or more processors; and a programmemory coupled to the one or more processors and storing executableinstructions that, when executed by the one or more processors, causethe computer system to: detect charge information associated with acharge level of a battery of the autonomous electric vehicle using theone or more sensors; determine the charge level of the battery basedupon the charge information; generate a predicted use profile for theautonomous electric vehicle based upon prior vehicle use data; determinea time and a location at which to charge the battery based upon thecharge level and the predicted use profile; control the autonomouselectric vehicle to travel fully autonomously to the determined locationat the determined time; cause the battery of the autonomous electricvehicle to charge at the location; determine a return location for thevehicle based upon the predicted use profile; and control the autonomouselectric vehicle to travel fully autonomously to the return location.12. The computer system of claim 11, wherein: the predicted use profileindicates a next predicted use of the autonomous electric vehicle; andthe time and location are determined when sufficient time exists torecharge the battery before the next predicted use.
 13. The computersystem of claim 11, wherein the location at which to charge the batteryis associated with a charging station selected from a plurality ofcharging stations based at least in part upon availability of theselected charging station.
 14. The computer system of claim 11, whereinthe return location is determined based upon the predicted use profileand is distinct from a prior location from which the autonomous electricvehicle travels to the location at which to charge the battery.
 15. Thecomputer system of claim 11, wherein: the executable instructionsfurther cause the computer system to: identify a current location of theautonomous electric vehicle using one or more geolocation componentswithin the autonomous electric vehicle; and identify one or morecharging stations in an area surrounding the current location from adatabase including location data for a plurality of charging stations;and the location at which to charge the battery is selected from thelocation data associated with the one or more charging stations based atleast in part upon distance from the current location.
 16. The computersystem of claim 11, wherein the predicted use profile indicates aplurality of use periods and use locations over at least one day.
 17. Atangible, non-transitory computer-readable medium storing executableinstructions for automatically recharging an autonomous electric vehiclethat, when executed by at least one processor of a computer system,cause the computer system to: detect charge information associated witha charge level of a battery of the autonomous electric vehicle using oneor more sensors disposed within the autonomous electric vehicle;determine the charge level of the battery based upon the chargeinformation; generate a predicted use profile for the autonomouselectric vehicle based upon prior vehicle use data; determine a time anda location at which to charge the battery based upon the charge leveland the predicted use profile; control the autonomous electric vehicleto travel fully autonomously to the determined location at thedetermined time; cause the battery of the autonomous electric vehicle tocharge at the location; determine a return location for the vehiclebased upon the predicted use profile; and control the autonomouselectric vehicle to travel fully autonomously to the return location.18. The tangible, non-transitory computer-readable medium of claim 17,wherein: the predicted use profile indicates a next predicted use of theautonomous electric vehicle; and the time and location are determinedwhen sufficient time exists to recharge the battery before the nextpredicted use.
 19. The tangible, non-transitory computer-readable mediumof claim 17, wherein the location at which to charge the battery isassociated with a charging station selected from a plurality of chargingstations based at least in part upon availability of the selectedcharging station.
 20. The tangible, non-transitory computer-readablemedium of claim 17, further storing instructions that cause the computersystem to: identify a current location of the autonomous electricvehicle using one or more geolocation components within the autonomouselectric vehicle; and identify one or more charging stations in an areasurrounding the current location from a database including location datafor a plurality of charging stations, wherein the location at which tocharge the battery is selected from the location data associated withthe one or more charging stations based at least in part upon distancefrom the current location.